OPTIMIZATION MODEL AND DSS FOR MAXIMUM RESOLUTION DICHOTOMIES

James K. Ho, Sydney C. K. Chu and S. S. Lam

A topological model is presented for complex data sets in which the attributes can be cast into a dichotomy. It is shown that the relative dominance of the two parts in such a dichotomy can be measured by the corresponding areas in its star plot. An optimization model is proposed to maximize the resolution of such a measure by choice of configuration of the attributes, as well as the angles among them. The approach is illustrated with the case of online auction markets, where there is a buyer-seller dichotomy as to whether conditions are favourable to buyers or sellers. An implementation of the methodology in a spreadsheet based DSS is demonstrated. Its ease of use is promising for diverse applications.

IMITATING THE KNOWLEDGE MANAGEMENT OF COMMUNITIES OF PRACTICE

Juan Pablo Soto, Aurora Vizcaíno, Javier Portillo, Oscar M. Rodríguez-Elias and Mario Piattini

Advances in technology have led to the development of knowledge management systems with the intention of improving organizational performance. Nevertheless, implementation of this kind of mechanisms is not an easy task due to the necessity of taking into account social aspects (such as reputation) that improve the exchange of information between groups of people. Considering, the advantages of working with groups with similar interests we have modelled communities of agents which represent communities of people interested in similar topics. In order to implement this model we propose a multi-agent architecture in charge of evaluating the relevance of the knowledge in a knowledge base and the degree of reputation that a person has as the contributor of information. We pay particular attention to showing how the use of the agents works by using a prototype system to search for knowledge related to a particular domain of a community of practice. Several communities of agents integrated into an organization have the capacity to follow the interaction process of employees when carrying out their daily activities.

NONLINEAR FUZZY SELFTUNING PID CONTROL TECHNOLOGY AND ITS APPLICATIONS IN AUTOMATED PROGRAMMING ROBOTICS

Ganwen Zeng and Qianglong Zeng

The paper presents an advanced Fuzzy self-tuning PID controller theory and it implement its applications on Data I/O’s automated robotic programming systems. Considerable programming technology shift occurred in recent device programmer industry; programming density have been constantly fast growing from low-volume to high-volume programming for all kinds of non-volatile flash memory devices such as NOR flash, NAND flash, and MMC cards, SD flash cards, serial flash device, serial flash cards, flash-based microcontrollers and flash disks as high performance M-systems DiskOnChip. Device programming mode is more demanding an automatic programming than manual operation mode. It drives the creation and implementation of a high-performance automated programming robotic systems. This paper shows how this proposed advanced Fuzzy self-tuning PID controllers work on these automated programming robotic automation systems.

MULTIVARIATE CONTROL CHARTS WITH A BAYESIAN NETWORK

Sylvain Verron, Teodor Tiplica and Abdessamad Kobi

The purpose of this article is to present an approach allowing the fault detection of a multivariate process with a bayesian network. As a discriminant analysis is easily modeled with a bayesian network, we will show that we we can consider the multivariate T2 and MEWMA control charts as particular cases of the discriminant analysis. So, we give the structure of the bayesian network as well as the parameters of the network in order to detect faults in the multivariate space in the same manners as if we used multivariate control charts. The resulting bayesian network, with a computed threshold, is similar to the multivariate control charts.

OBJECT LIST CONTROLLED PROCESS DATA SYSTEM

Anton Scheibelmasser and Bernd Eichberger

The appropriate design of a system is one of the essential topics at the beginning of a new development project. According to the intended purpose of a device the first step is to model the system in order to get a structure for the implementation of the required features. In general the implementation of the system requirements is split in hardware parts and tasks which are done in software. In case of the hardware design the solutions for the challenges are mostly clear and supported by fundamentals of e.g. digital logic laws and several design methods. If we think of the software part a lot of problems have to be solved without such clear fundamentals. Object oriented design is one of the paradigms which promise a way for designing stable and reliable software. A problem arises in this context if the used microprocessor platform is not supported with a compiler for an object oriented programming language. In this case only the system modelling could be done in terms of software objects and their relations, the implementation has to be done in a procedural language. The following article is based on research work done in the development of a modular process data system. Based on a sequential main program and interrupt driven hardware interfaces, a software implementation without an operating system was implemented. By means of special software structure called linked object list, object oriented design was implemented with the procedural language “C”. Due to this design a reusable and flexible system was achieved which enables a high degree of flexibility concerning the hardware configuration and system customization at the user site.

DETECTION OF THE NEED FOR A MODEL UPDATE IN STEEL MANUFACTURING

Heli Koskimäki (née Junno), Ilmari Juutilainen, Perttu Laurinen and Juha Röning

When new data are obtained or simply when time goes by, the prediction accuracy of models in use may decrease. However, the question is when prediction accuracy has dropped to a level where the model can be considered out of date and in need of updating. This article describes a method that was developed for detecting the need for a model update. The method is applied in the steel industry, and the model whose need of updating is under study is a regression model developed to model the yield strength of steel plates. It is used to plan process settings in steel plate product manufacturing. To decide on the need for updating, information from similar past cases was utilized by introducing a limit called an exception limit. The limit was used to indicate when a new observation was from an area of the model input space where the prediction errors of the model have been too high. Moreover, an additional limit was formed to indicate when too many exceedings of the exception limit have occurred within a certain time scale. These two limits were then used to decide when to update the model.

DIGITAL PATTERN SEARCH AND ITS HYBRIDIZATION WITH GENETIC ALGORITHMS FOR GLOBAL OPTIMIZATION

Nam-Geun Kim, Youngsu Park and Sang Woo Kim

In this paper, we present a new approach of evolutionary algorithm called genetic pattern search algorithm (GPSA). The proposed algorithm is closely related to genetic algorithms which use binary-coded genes. The main contribution of this paper is to propose binary-coded pattern called digital pattern which is transformed from real-coded pattern in general pattern search methods. Moreover we offer self-adapting genetic algorithm by adopting digital pattern that modifies the step size and encoding esolutions of previous optimization procedure, and chases the optimal pattern's direction. In addition, we compare GPSA with genetic algorithm in the robustness and the performances of optimization. All experiments employ the well-known benchmark functions whose functional values and coordinates of each global minimum are already reported.

DISTRIBUTED EMBEDDED SYSTEM FOR ULTRALIGHT AIRPLANE MONITORING

J. Kotzian and V. Srovnal Jr.

This paper presents distributed embedded monitoring system that is developed for small aircrafts, sports airplane and ultralights airplanes. System is made from modules connected by industrial bus CAN. This low cost system is trying to solve bad situation with many ultralights without any digital measurement unit due to their prices. The contribution shows basic architecture of the embedded monitoring system and presents some parts of hardware and software implementation. The interface between aviator and airplane is established using graphic user interface based on operating system uClinux.

DISCRETE DYNAMIC SLIDING SURFACE CONTROL FOR ROBUST SPEED CONTROL OF INDUCTION MACHINE DRIVE

Abdel Faqir, Daniel Pinchon, Rafiou Ramanou and Sofiane Mahieddine

This paper proposes the discrete dynamic sliding surface control to guarantee the existence of discrete sliding mode and reduce the chattering phenomena for speed control of induction machine drive. In discrete systems, the controller does not control the system during the sampling interval. The great chattering and large control signal are caused by the high switching gain. In this paper, the dynamic sliding surface is introduced to overcome the drawback. By setting the initial value of the dynamic sliding surface, the system can lock to the sliding surface quickly without high switching gain. The control signal can be reduced and the chattering can be eliminated. Furthermore, the induction machine speed control system is used to show this controller’s robustness to against the parameter variation and external load. The speed of the induction machine is regulated using the indirect field oriented control (IFOC). Thus, after the application of the IFOC technique by determining the decoupled model of the machine, a discrete sliding surface controller has been applied. Simulation study is used to show the performances of the proposed method and then validated by an experimental prototype.

BOUNDARY CONTROL OF A CHANNEL - Last Improvements

Valérie dos Santos and Christophe Prieur

Different improvements have been developed in regards to the stability and the control of two-by-two non linear systems of conservation laws, and in particular for the Saint-Venant equations and the control of flow and water level on irrigation channel. One stability result based on the Riemann coordinates is presented here and sufficient conditions are given to insure the Cauchy convergence. Another result still based on the Riemann approach is presented too, in the linear case, to improve the feedback control based on the Riemann invariants.

EFFECTIVE GENETIC OPERATORS OF COOPERATIVE GENETIC ALGORITHM FOR NURSE SCHEDULING

Makoto Ohki, Shin-ya Uneme, Shigeto Hayashi and Masaaki Ohkita

This paper proposes effective genetic operators for cooperative genetic algorithm (GA) to solve a nurse scheduling problem. In hospitals, 15-30 nurses are assigned to any section such as the internal medicine department or the pediatrics department. A clinical director of the department makes a duty schedule of all nurses of the department every month. Such the scheduling is very complex task. It takes one or two weeks to create the nurse schedule even by a veteran director. Recently, computer software creating the nurse schedule is developed to reduce such the problem. Even when the newest commercial software creates and optimizes the nurse schedule, it needs more than one or two hours. Since this is very risky for the users, an algorithm giving a solution in a shorter running time is still required. In conventional ways using the cooperative GA, a crossover operator is only employed for the optimization, because it does not lose consistency between chromosomes. We propose mutation operator and virus operator for the cooperative GA, which does not lose consistency of the nurse schedule. The cooperative GA with these new operators has brought a surprisingly good result, it has never been brought by the conventional algorithm.

BINARY OPTIMIZATION: A RELATION BETWEEN THE DEPTH OF A LOCAL MINIMUM AND THE PROBABILITY OF ITS DETECTION

B. V. Kryzhanovsky, V. M. Kryzhanovsky and A. L. Mikaelian

The standard method in optimization problems consists in a random search of the global minimum: a neuron network relaxes in the nearest local minimum from some randomly chosen initial configuration. This procedure is to be repeated many times in order to find as deep an energy minimum as possible. However the question about the reasonable number of such random starts and whether the result of the search can be treated as successful remains always open. In this paper by analyzing the generalized Hopfield model we obtain expressions describing the relationship between the depth of a local minimum and the size of the basin of attraction. Based on this, we present the probability of finding a local minimum as a function of the depth of the minimum. Such a relation can be used in optimization applications: it allows one, basing on a series of already found minima, to estimate the probability of finding a deeper minimum, and to decide in favor of or against further running the program. The theory is in a good agreement with experimental results.

A NEW LOAD ADJUSTMENT APPROACH FOR JOB-SHOPS

Z. Bahroun, J.-P. Campagne and M. Moalla

This paper presents a new load adjustment approach by overlapping for a set of jobs in a job-shop context, guaranteeing the existence of a limited capacity schedule without scheduling under the assumption of pre-emptive tasks. This approach is based on the exploitation of the tasks scheduling time segments overlapping and on the distribution of the job’s margins between tasks in a just in time context. First, we present a literature review concerning load adjustment approaches. Second, we introduce the overlapping load adjustment approach. Third, we present an original heuristic to use this approach in the case of job-shops organized firms. After that, we present the scheduling approach. Finally, we will discuss a more general use of this approach and the possible extensions.

SATURATION FAULT-TOLERANT CONTROL FOR LINEAR PARAMETER VARYING SYSTEMS

Ali Abdullah

This paper presents a methodology for designing a fault-tolerant control (FTC) system for linear parameter varying (LPV) systems subject to actuator saturation fault. The FTC system is designed using linear matrix inequality (LMI) and model estimation techniques. The FTC system consists of a nominal control, fault diagnostic, and fault accommodation schemes. These schemes are designed to achieve stability and tracking requirements, estimate a fault, and reduce the fault effect on the system. Simulation studies are used to illustrate the proposed design.

LINEAR PROGRAMMING FOR DATABASE ENVIRONMENT

Akira Kawaguchi and Jose Alfredo Perez

Solving large-scale optimization problems requires an integration of data-analysis and data-manipulation capabilities. Today's databases support real-time decision analysis and complex decision making. Nevertheless, little attempt has been made to facilitate general linear programming solvers for database environments. Dozens of sophisticated tools and software libraries that implement linear programming model can be found. But, there is no database-embedded linear programming tool seamlessly and transparently utilized for database processing. The focus of this study is to fill out this kind of technical gap of data analysis and data manipulation, in the event of solving large-scale linear programming problems for the applications built on the database environment. Specifically, this paper studies the representation of the linear programming model in relational structures and the computational method to solve the linear programming problems. This development is critical in the circumstances of the wide applicability of the linear programming problems to today's database applications. Foundations for and preliminary experimental results of this study are presented.

BREAKING ACCESSIBILITY BARRIERS - Computational Intelligence in Music Processing for Blind People

Wladyslaw Homenda

A discussion on involvement of knowledge based methods in implementation of user friendly computer programs for disabled people is the goal of this paper. The paper presents a concept of a computer program that is aimed to aid blind people dealing with music and music notation. The concept is solely based on computational intelligence methods involved in implementation of the computer program. The program is build around two research fields: information acquisition and knowledge representation and processing which are still research and technology challenges. Information acquisition module is used for recognizing printed music notation and storing acquired information in computer memory. This module is a kind of the paper-to-memory data flow technology. Acquired music information stored in computer memory is then subjected to mining implicit relations between music data, to creating a space of music information and then to manipulating music information. Storing and manipulating music information is firmly based on knowledge processing methods. The program described in this paper involves techniques of pattern recognition and knowledge representation as well as contemporary programming technologies. It is designed for blind people: music teachers, students, hobbyists, musicians.

MULTICRITERIA DECISION MAKING IN BALANCED MODEL OF FUZZY SETS

Wladyslaw Homenda

In the paper aspects of negative information and of information symmetry in context of uncertain information processing is considered. Both aspects are presented in frames of fuzzy sets theory involved in data aggregation and decision making process. Asymmetry of classical fuzziness and its orientation to positive information are pointed out. The direct dependence of symmetry of uncertain information on negative information maintenance is indicated. The symmetrical, so called balanced, extension of classical fuzzy sets integrating positive and negative information an paralleling positiveness/negativeness with symmetry of fuzziness is presented. Balanced counterparts of classical fuzzy connectives are introduced.

MORE EXPRESSIVE PLANNING GRAPH EXTENSION

Joseph Zalaket and Guy Camilleri

Since its appearance Graphplan allured the researchers in AI planning for its compact structure. In addition to its performance to solve planning problems, Graphplan has served many heuristic planners by its planning graph structure. Many extensions have been made to the Graphplan or to its planning graph to enhance their performance and to make them able to solve new type of knowledge like temporal and resources. The most of these extensions have treated the temporal and numeric resource knowledge as a foreign body incorporated into Graphplan. Our deep observation to the Graphplan structure showed us that this structure is able to deal with all kind of knowledge by the same way as with symbolic knowledge. Even more, this structure is able to handle black box functions which manipulate all kind of data. In this paper, we present a variation of Graphplan which supports the execution of external functions for numeric knowledge update. This variation allows Graphplan to run all kind of knowledge using its original planning graph as the base of the data structure.

NONLINEAR MODEL PREDICTIVE CONTROL OF A LINEAR AXIS BASED ON PNEUMATIC MUSCLES

Harald Aschemann and Dominik Schindele

This paper presents a nonlinear optimal control scheme for a mechatronic system consisting of a guided carriage driven by an antagonistic pair of pneumatic muscle actuators. Modelling leads to a system of nonlinear differential equations including polynomial approximations of the volume characteristic as well as the force characteristic of the pneumatic muscles. The proposed control has a cascade structure. The nonlinear norm-optimal control of both pneumatic muscle pressures is based on an approximative solution of the corresponding HJB-equation, whereas the outer control loop involves a multivariable NMPC of the carriage position and the mean internal pressure of the pneumatic muscles. To improve the tracking behaviour, the feedback control loops are extended with nonlinear feedforward control based on differential flatness. Remaining model uncertainties as well as nonlinear friction can be counteracted by an observer-based disturbance compensation. Experimental results from an implementation on a test rig show an excellent control performance.

GENETIC REINFORCEMENT LEARNING OF FUZZY INFERENCE SYSTEM APPLICATION TO MOBILE ROBOTIC

Abdelkrim Nemra, Hacene Rezine and Abdelkrim Souici

An efficient genetic reinforcement learning algorithm for designing Fuzzy Inference System (FIS) with out any priory knowledge is proposed in this paper. Reinforcement learning using Fuzzy Q-Learning (FQL) is applied to select the consequent action values of a fuzzy inference system, in this method, the consequent value is selected from a predefined value set which is kept unchanged during learning and if the optimal solution is not present in the randomly generated set, then the performance may be poor. Also genetic algorithms (Genetic Algorithm) are performed to on line search for better consequent and premises parameters based on the learned Q-values as adaptation function. In Fuzzy-Q-Learning Genetic Algorithm (FQLGA), memberships (premises) parameters are distributed equidistant and the consequent parts of fuzzy rules are randomly generated. The algorithm is validated in simulation and experimentation on mobile robot reactive navigation behaviors.

DEFECT-RELATED KNOWLEDGE ACQUISITION FOR DECISION SUPPORT SYSTEMS IN ELECTRONICS ASSEMBLY

Sébastien Gebus and Kauko Leiviskä

Real-time process control and production optimization are extremely challenging areas. Traditional approaches often do not work due to a lack of robustness or reliability, especially when dealing with incomplete, inaccurate, or simply irrelevant data. This is a major problem when building decision support systems especially in electronics manufacturing, where it is quite common to have large databases and run blindly feature extraction and data mining methods. Performance of these methods could, however, be drastically increased when combined with knowledge or expertise of the process. This paper describes how defect-related knowledge on an electronic assembly line can be integrated in the decision making process at an operational and organizational level. It focuses in particular on the efficient acquisition of shallow knowledge concerning everyday human interventions on the production lines, as well as on the conceptualization and factory wide sharing of the resulting defect information. Software with dedicated interfaces has been developed for that purpose. Semi-automatic knowledge acquisition from the production floor and generation of comprehensive reports for the quality department resulted in an improvement of the usability, usage, and usefulness of the decision support system.

A NEURAL-CONTROL SYSTEM FOR A HUMANOID ARTIFICIAL ARM

Michele Folgheraiter, Giuseppina Gini and Massimo Cavallari

In this paper we illustrate the architecture and the main features of a bio-inspired control system employed to govern an anthropomorphic artificial Arm. The manipulation system we developed was designed starting from an attentive study of the human limb from the anatomical, physiological and neurological point of view. In accordance with the general view of the Biorobotics field we try to replicate the structure and the functionalities of the natural limb. Thanks to this biomimetic approach we obtained a system that can perform movements similar to those of the natural limb. The control system is organized in a hierarchical way. The low level controller emulates the neural circuits located in the human spinal cord and is charged to reproduce the reflexes behaviors and to control the arm stiffness. The high level control system generates the arm trajectory performing the inverse kinematics and furnishing the instantaneous muscles reference position. In particular we implemented the Inverse kinematic using a gradient based algorithm; at each step the actuators movements are arranged in order to decrease the distance between the wrist and the target position. Simulation and experimental results shows the ability of the control system in governing the arm to follow a predefined trajectory and to perform human like reflexes behaviors.

COMPARYING A TABU SEARCH PROCESS - Using and Not Using and Intensification Strategy to Solve the Vehicle Routing Problem

Etiene Pozzobom Lazzeris Simas and Arthur Tórgo Gómez

In this paper we propose a Tabu Search algorithm to solve the Vehicle Routing Problem. The Vehicle Routing Problem are usually defined as the problem that concerns in creation of least cost routs to serve a set of clients by a fleet of vehicles. We develop an intensifications strategy to diversify the neighbours generated and to increase the neighbourhood size. We had done experiments using and not using the intensification strategy to compare the performance of the search. The experiments we had done showed that an intensification strategy allow an increase on the solutions quality.

A DISCRETE-EVENT SYSTEM APPROACH TO MULTI-AGENT DISTRIBUTED CONTROL OF CONTAINER TERMINALS

Guido Maione

The area of managing and controlling intermodal terminal systems is relatively new. The paradigms of Discrete Event Systems for modelling purpose and of Multi-Agent Systems for distributed control seem promising. Many research attempts have been made to develop modelling and simulation tools but no standard exists. This paper presents a Discrete Event System model of the agents introduced to describe how a distributed control of the terminal activities can be achieved. The interaction mechanism between four classes of agents is modelled in detail. The approach is useful to develop a simulation platform to test MAS efficiency in terminal management and to measure the performance of static or adapted control strategies.

A DISTRIBUTED REINFORCEMENT LEARNING CONTROL ARCHITECTURE FOR MULTI-LINK ROBOTS - Experimental Validation

Jose Antonio Martin H. and Javier De Lope

A distributed approach to Reinforcement Learning (RL) in multi-link robot control tasks is presented. One of the main drawbacks of classical reinforcement learning is the combinatorial explosion when multiple states variables and multiple actuators are needed to optimally control a complex agent in a dynamical environment. In this paper we present an approach to avoid this drawback based on a distributed RL architecture. The experimental results in learning a control policy for diverse kind of multi-link robotic models clearly shows that it is not necessary that each individual RL-agent perceives the complete state space in order to learn a good global policy but only a reduced state space directly related to its own environmental experience. The proposed architecture combined with the use of continuous reward functions results of an impressive improvement of the learning speed making tractable some learning problems in which a classical reinforcement learning with discrete rewards (-1,0,1) does not work.

ROBUST ADAPTIVE WAVELET NEURAL NETWORK TO CONTROL A CLASS OF NONLINEAR SYSTEMS

A. Hussain, N. Essounbouli, A. Hamzaoui and J. Zaytoon

This paper deals with the synthesis of a Wavelet Neural Network adaptive controller for a class of second order systems. Due to its fast convergence, the wavelet neural network is used to approximate the unknown dynamics, which will be on-line adjusted according to the adaptation laws deduced from the stability analysis. To ensure the robustness of the closed loop system, a modified sliding mode control signal is used. In this work, variable sliding surface is considered to reduce the starting energy without deteriorating the tracking performances. Furthermore, the knowledge of the upper bounds of both the external disturbances and the approximation errors is not needed. The global stability of the closed loop system is guaranteed in the sense of Lyapunov. Finally, a simulation example is presented to illustrate the efficiency of the

PATTERN-DRIVEN REUSE OF EMBEDDED CONTROL DESIGN - Behavioral and Architectural Specifications in Embedded Control System Designs

Miroslav Sveda, Ondrej Rysavy and Radimir Vrba

This paper deals with reuse of architectural and behavioral specifications of embedded systems employing finite-state and timed automata. The contribution proposes not only how to represent a system’s formal specification as an application pattern structure of specification fragments, but also how to measure similarity of formal specifications for retrieval with case-based reasoning support. The paper provides also an insight into case-based reasoning support as applied to formal specification reuse by application patterns built on finite-state and timed automata. Those application patterns create a base for a pattern language supporting reuse-oriented design process for a class of real-time embedded systems.

A SERVICE-ORIENTED FRAMEWORK FOR MANNED AND UNMANNED SYSTEMS TO SUPPORT NETWORK-CENTRIC OPERATIONS

Norbert Oswald, André Windisch, Stefan Förster, Herwig Moser and Toni Reichelt

Network-centricity and autonomy are two buzzwords that have found increasing attention since the beginning of this decade in both, the military and civil domain. Although various conceptions exist of which capabilities are required for a system to be considered network-centric or autonomous, there can hardly be found proposals or prototypes that describe concrete transformations for both capabilities into software. The presented paper reviews work accomplished at EADS Military Air Systems driven by the need to develop an infrastructure that supports the realisation of both concepts in software with respect to traditional and modern software engineering principles, e.g., re-use and service-oriented development. This infrastructure is provided in form of a prototypical framework, accompanied by configuration and monitoring tools. Tests in a complex scenario requiring network-centricity and autonomy have shown that a significant technical readiness level can be reached by using the framework for mission software development.

A FUZZY PARAMETRIC APPROACH FOR THE MODEL-BASED DIAGNOSIS

F. Lafont, N. Pessel and J. F. Balmat

This paper presents a new approach for the model-based diagnosis. The model is based on an adaptation with a variable forgetting factor. The variation of this factor is managed thanks to fuzzy logic. Thus, we propose a design method of a diagnosis system for the sensors defaults. In this study, the adaptive model is developed theoretically for the Multiple-Input Multiple-Output (MIMO) systems. We present the design stages of the fuzzy adaptive model and we give details of the Fault Detection and Isolation (FDI) principle. This approach is validated with a benchmark: an hydraulic process with three tanks. Different defaults (sensors) are simulated with the fuzzy adaptive model and the fuzzy approach for the diagnosis is compared with the residues method. The first results obtained are promising and seems applicable on a set of MIMO systems.

TRACKING CONTROL DESIGN FOR A CLASS OF AFFINE MIMO TAKAGI-SUGENO MODELS

Carlos Ariño, Antonio Sala and Jose Luis Navarro

When controlling Takagi-Sugeno fuzzy systems, verification of some sector conditions is usually assumed. However, setpoint changes may alter the sector bounds. Alternatively, setpoint changes may be considered as an offset addition in many cases, and hence affine Takagi-Sugeno models may be better suited to this problem. This work discusses a nonconstant change of variable in order to carry out offset-ellimination in a class of MIMO canonical affine Takagi-Sugeno models. Once the offset is cancelled, standard fuzzy control design techniques can be applied for arbitrary setpoints. The canonical models studied use as state representation a set of basic variables and their derivatives. Some examples are included to illustrate the procedure.

BEHAVIOUR NAVIGATION LEARNINIG USING FACL ALGORITHM

Abdelkarim Souissi and Hacene Rezine

In this article, we are interested in the reactive behaviours navigation training of a mobile robot in an unknown environment. The control consists in bringing the robot in a given position, avoiding obstacles and releasing it from the tight corners and deadlock obstacles shape. In this framework, we used the reinforcement learning (FACL) method, or Fuzzy Actor-Critic learning based on temporal differences prediction method (TD). It allows the output adaptation of fuzzy inference system apprentice (SIF) in response to the rewards and punishments which it receives when interacting with the environment. The system has continuous type states and actions.

A JOINT HIERARCHICAL FUZZY-MULTIAGENT MODEL DEALING WITH ROUTE CHOICE PROBLEM - RoSFuzMAS

Habib M. Kammoun, Ilhem Kallel and Adel M. Alimi

Nowadays, multiagent architectures and traffic simulation agent-based are the most promising strategies for intelligent transportation systems. This paper presents a road supervision model based on fuzzy-multiagent system and simulation, called RoSFuzMAS. Thanks to agentification of all components of the transportation system, dynamic agents interact to provide real time information and a preliminary choice of advised routes. To ensure the model rationality, and to improve the route choice make decision, we propose to use a hierarchical Fuzzy inference including some pertinent criteria handling the environment as well as the driver behavior. A multiagent simulator with graphic interface has been achieved to visualize, test and discuss our road supervision system. Experimental results demonstrate the capability of RoSFuzMAS to perform a dynamic path choice minimizing traffic jam occurrences by combining multiagent technology and real time fuzzy behaviors.

TARGET VALUE PREDICTION FOR ONLINE OPTIMIZATION AT ENGINE TEST BEDS

Alexander Sung, Andreas Zell, Florian Kl¨opper, Alexander Vogel

The settling times of target functions play an important role in the domain of online optimization at the engine test bed. Inert target functions generally induce long measuring times which lead to increased costs. In this article, we analyze how previous knowledge about the physical behavior of target functions can be used to early predict the final steady state value to reduce measuring times.

DISCRETE GENETIC ALGORITHM AND REAL ANT COLONY OPTIMIZATION FOR THE UNIT COMMITMENT PROBLEM

Guillaume Sandou

In this paper, a new cooperative metaheuristic for the solution of the classical Unit Commitment problem is presented. This problem is known to be an often large scale, mixed integer programming problem. Due to the curse of combinatorial complexity, the exact solution is often intractable. Thus, a metaheuristic based method has to be used to compute a very often suitable solution with low computation times. A new approach is presented here. The main idea is to couple a genetic algorithm to compute binary variables (on/off status of units), and an ant colony based algorithm to compute real variables (produced powers). Finally, results show that the cooperative method leads to the tractable computation of a satisfying solution for the Unit Commitment problem.

NEW RESULTS ON DIAGNOSIS BY FUZZY PATTERN RECOGNITION

Mohamed Saïd Bouguelid, Moamar Sayed Mouchaweh and Patrice Billaudel

We use the classification method Fuzzy Pattern Matching (FPM) to realize the industrial and medical diagnosis. FPM is marginal, i.e., its global decision is based on the selection of one of the intermediate decisions. Each intermediate decision is based on one attribute. Thus, FPM does not take into account the correlation between attributes. Additionally, FPM considers the shape of classes as convex one. These drawbacks make FPM unusable for many real world applications. In this paper, we propose to improve FPM to solve these drawbacks. Several synthetic and real data sets are used to show the performances of the Improved FPM (IFPM) with respect to classical one as well as to the well known classification method K Nearest Neighbours (KNN). KNN is known to be preferment in the case of data represented by correlated attributes or by classes with non convex shape.

INVERSION OF A SEMI-PHYSICAL ODE MODEL

Laurent Bourgois, Gilles Roussel and Mohammed Benjelloun

This study proposes to examine the performances of an inverse dynamic model by fusion of statistical training and deterministic modeling. We carry out an inverse semi-physic model using a recurrent neural network. The structure of this network is guided by preliminary search of a reverse discrete state form of the direct model. The performances in term of generalization, regularization and training effort are highlighted compared to the reduction in parameters to estimate of the neural network. Some tests are carried out on a simple second order model, but the form of a dynamic system characterized by an ordinary differential equation of an unspecified $r$ order is proposed.

TAKAGI-SUGENO MULTIPLE-MODEL CONTROLLER FOR A CONTINUOUS BAKING YEAST FERMENTATION PROCESS

Enrique Herrera, Bernardino Castillo, Jesús Ramírez and Eugénio C. Ferreira

The purpose of this work is to design a fuzzy integral controller to force the switching of a bioprocess between two different metabolic states. A continuous baker’s yeast culture is divided in two sub-models: a respiro-fermentative with ethanol production and a respirative with ethanol consumption. The switching between both different metabolic states is achieved by means of tracking a reference substrate signal. A substrate fuzzy integral controller model using sector nonlinearity was built for both nonlinear models; the controller gains were designed using Linear Matrix Inequalities (LMI’s).

TOWARDS RELIABLE AUTOFOCUSING IN AUTOMATED MICROSCOPY

Silvie Luisa Brázdilová

The results presented in this paper are twofold. First, autofocusing in automated microscopy is studied and evaluated with respect to biomedical samples whose images can have more focal planes. While the proposed procedure for finding the maximum of a focus function in a short time works satisfactorily, the focus function itself is identified as the weakest link of the whole process. Second, an interesting property of functions used for genetic programming, and an algorithm for generating new individuals are introduced. Their usefulness and applicability are demonstrated on the problem of finding a new focus function for automated autofocusing in microscopy.

A HYBRID INTELLIGENT MULTI-AGENT METHOD FOR MONITORING AND FAULTS DIAGNOSIS

Gang Yao and Tianhao Tang

This paper presents a hybrid intelligent multi-agent method for monitoring and faults diagnosis. A new diagnosis process, combined with data mining and neural networks, are discussed as well as the functions and structure of agent which implements these algorithms. At last, some simulation results are shown to demonstrate the efficiency of the proposed system.

SENSOR-ASSISTED ADAPTIVE MOTOR CONTROL UNDER CONTINUOUSLY VARYING CONTEXT

Heiko Hoffmann, Georgios Petkos, Sebastian Bitzer and Sethu Vijayakumar

Adaptive motor control under continuously varying context, like the inertia parameters of a manipulated object, is an active research area that lacks a satisfactory solution. Here, we present and compare three novel strategies for learning control under varying context and show how adding tactile sensors may ease this task. The first strategy uses only dynamics information to infer the unknown inertia parameters. It is based on a probabilistic generative model of the control torques, which are linear in the inertia parameters. We demonstrate this inference in the special case of a single continuous context variable -- the mass of the manipulated object. In the second strategy, instead of torques, we use tactile forces to infer the mass in similar way. Finally, the third strategy omits this inference -- which may be infeasible if the latent space is multi-dimensional -- and directly maps the state, state transitions, and tactile forces onto the control torques. The additional tactile input implicitly contains all control-torque relevant properties of the manipulated object. In simulation, we demonstrate that this direct mapping can provide accurate control torques under multiple varying context variables.

SETPOINT ASSIGNMENT RULES BASED ON TRANSFER TIME DELAYS FOR WATER-ASSET MANAGEMENT OF NETWORKED OPEN-CHANNEL SYSTEMS

Eric Duviella, Pascale Chiron and Philippe Charbonnaud

The paper presents a new strategy based on a supervision and hybrid control accommodation to improve the water-asset management of networked open-channel systems. This strategy requires a modelling method of the network based on a weighted digraph of instrumented points, and the definition of resource allocation and setpoint assignment rules. Two setpoint assignment rules are designed and evaluated in the case of an open-channel system composed of one difluent and one confluent showing their effectiveness.

DISTRIBUTED CONTROL ARCHITECTURE FOR AUTOMATED NANOHANDLING

Christian Stolle

New distributed control architecture for micro- and nanohandling cells is presented. As a modular system it is designed to handle micro- and nanorobotic automation tasks at semi- up to full automation level. The architecture includes different visual sensors as there are scanning electron microscopes (SEM) and CCD cameras for position tracking as well as non-optical force, temperature, etc. sensors for environmental control. It allows usage of multiple nanorobots in parallel for combined autonomous fabrication tasks. The system provides a unified framework for mobile platforms and linear actors.

MODELING WITH CURRENT DYNAMICS AND VIBRATION CONTROL OF TWO PHASE HYBRID STEPPING MOTOR IN INTERMITTENT DRIVE

Ryota Mori, Yoshiyuki Noda, Takanori Miyoshi, Kazuhiko Terashima, Masayuki Nishida and Naohiko Suganuma

This paper presents modeling of stepping motor and control design of input pulse timing for the suppression control of vibration. The stepping motor has the transient response of electric current for the pulse input. Therefore, the motor model considering the transient response of the current is built. The validity of the proposed model is verified by comparing the model considering the transient response of the current with the one without its consideration. Design of the pulse input timing in the method of the four pulse drive is realized to achieve the desired angle without vibration and overshoot using an optimization method. Finally, the effectiveness of the proposed method is demonstrated by comparing simulation results with experiments.

PIECEWISE CONSTANT REINFORCEMENT LEARNING FOR ROBOTIC APPLICATIONS

Andrea Bonarini, Alessandro Lazaric and Marcello Restelli

Writing good behaviors for mobile robots is a hard task that requires a lot of hand tuning and often fails to consider all the possible configurations that a robot may face. By using reinforcement learning techniques a robot can improve its performance through a direct interaction with the surrounding environment and adapt its behavior in response to some non-stationary events, thus achieving a higher degree of autonomy with respect to pre-programmed robots. In this paper, we propose a novel reinforcement learning approach that addresses the main issues of learning in real-world robotic applications: experience is expensive, explorative actions are risky, control policy must be robust, state space is continuous. Preliminary results performed on a real robot suggest that on-line reinforcement learning, matching some specific solutions, can be effective also in real-world physical environments.

NONLINEAR PROGRAMMING IN APPROXIMATE DYNAMIC PROGRAMMING - Bang-bang Solutions, Stock-management and Unsmooth Penalties

Olivier Teytaud and Sylvain Gelly

Many stochastic dynamic programming tasks in continuous action-spaces are tackled through discretization. We here avoid discretization; then, approximate d ynamic programming (ADP) involves (i) many learning tasks, performed here by Support Vector Machines, for Bellman-function-regression (ii) many non-linear-o ptimization tasks for action-selection, for which we compare many algorithms. We include discretizations of the domain as particular non-linear-programming- tools in our experiments, so that by the way we compare optimization approaches and discretization methods. We conclude that robustness is strongly required in the non-linear-optimizations in ADP, and experimental results show that (i) discretization is sometimes inefficient, but some specific discretization is very efficient for "bang-bang" problems (ii) simple evolutionary tools outperform quasi-random in a stable manner (iii) gradient-based techniques are much less stable (iv) for most high-dimensional "less unsmooth" problems Covariance-Matrix-Adaptation is first ranked.

ACTIVE LEARNING IN REGRESSION, WITH APPLICATION TO STOCHASTIC DYNAMIC PROGRAMMING

Olivier Teytaud, Sylvain Gelly and Jérémie Mary

We study active learning as a derandomized form of sampling. We show that full derandomization is not suitable in a robust framework, propose partially derandomized samplings, and develop new active learning methods (i) in which expert knowledge is easy to integrate (ii) with a parameter for the exploration/exploitation dilemma (iii) less randomized than the full-random sampling (yet also not deterministic). Experiments are performed in the case of regression for value-function learning on a continuous domain. Our main results are (i) efficient partially derandomized point sets (ii) moderate-derandomization theorems (iii) experimental evidence of the importance of the frontier (iv) a new regression-specific user-friendly sampling tool less-robust than blind samplers but that sometimes works very efficiently in large dimensions. All experiments can be reproduced by downloading the source code and running the provided command line.

DC MOTOR FAULT DIAGNOSIS BY MEANS OF ARTIFICIAL NEURAL NETWORKS

Krzysztof Patan, Józef Korbicz and Gracjan Głowacki

The paper deals with a model-based fault diagnosis for a DC motor realized using artificial neural networks. Modelling of the considered process was carried out by using a neural network composed of dynamic neuron models. Decision making about possible faults was performed using statistical analysis of a residual. A neural network was applied to density shaping of a residual, and after that, assuming a significance level, a threshold was calculated. Moreover, to isolate faults a neural classifier was developed. The proposed approach was tested in DC motor laboratory systems at the nominal operations condition as well as in the case of faults.

HEURISTIC ALGORITHMS FOR SCHEDULING IN A MULTIPROCESSOR TWO-STAGE FLOWSHOP WITH 0-1 RESOURCE REQUIREMENTS

Ewa Figielska

This paper deals with the problem of preemptive scheduling in a two-stage flowshop with parallel unrelated machines at the first stage and a single machine at the second stage. At the first stage, jobs use some additional resources which are available in limited quantities at any time. The resource requirements are of 0-1 type. The objective is the minimization of makespan. The problem is NP-hard. Heuristic algorithms are proposed which, while solving to optimality the resource constrained scheduling problem at the first stage of the flowshop, select for simultaneous processing jobs according to rules promising a good (short) schedule in the flowshop. Several rules of job selection are considered. The performance of the proposed heuristic algorithms is analyzed by comparing their results with the lower bound on the optimal makespan. The results of computational experiments show that these heuristics are able to produce near-optimal solutions in short computational time.

AN INTELLIGENT MARSHALING PLAN BASED ON MULTI-POSITIONAL DESIRED LAYOUT IN CONTAINER YARD TERMINALS

Yoichi Hirashima

This paper proposes a new scheduling method for a marshaling in the container yard terminal. The proposed method is derived based on Q-Learning algorithm considering the desired position of containers that are to be loaded into a ship. In the method, 3 processes can be optimized simultaneously: rearrangement order of containers, layout of containers assuring explicit transfer of container to the desired position, and removal plan for preparing the rearrange operation. Moreover, the proposed method generates several desired positions for each container, so that the learning performance of the method can be improved as compared to the conventional methods. In general, at container yard terminals, containers are stacked in the arrival order. Containers have to be loaded into the ship in a certain order, since each container has its own shipping destination and it cannot be rearranged after loading. Therefore, containers have to be rearranged from the initial arrangement into the desired arrangement before shipping. In the problem, the number of container-arrangements increases by the exponential rate with increase of total count of containers, and the rearrangement process occupies large part of total run time of material handling operation at the terminal. For this problem, conventional methods require enormous time and cost to derive an admissible result. In order to show effectiveness of the proposed method, computer simulations for several examples are conducted.

SCHEDULING OF MULTI-PRODUCT BATCH PLANTS USING REACHABILITY ANALYSIS OF TIMED AUTOMATA MODELS

Subanatarajan Subbiah, Sebastian Panek, Sebastian Engell and Olaf Stursberg

Standard scheduling approaches in process industries are often based on algebraic problem formulations solved as MI(N)LP optimization problems to derive production schedules. To handle such problems techniques based on timed automata have emerged recently. This contribution reports on a successful application of a new modeling scheme to formulate scheduling problems in process industries as timed automata (TA) models and describes the solution technique to obtain schedules using symbolic reachability analysis. First, the jobs, resources and additional constraints are modeled as sets of synchronized timed automata. Then, the individual automata are composed by parallel composition to form a global automaton which has an initial location where no jobs have been started and at least one target location where all jobs have been finished. A cost optimal symbolic reachability analysis is performed on the composed automaton to derive schedules. The main advantage of this approach over other MILP techniques is the intuitive graphical and modular modeling and the ability to compute better solutions within reasonable computation time. This is illustrated by a case study.

AUTOMATIC ESTIMATION OF PARAMETERS FOR THE HIERARCHICAL REDUCTION OF RULES OF COMPLEX FUZZY CONTROLLERS

Yulia Ledeneva, Carlos A. Reyes-García and Alejandro Díaz-Méndez

Fuzzy control is an imitation of the fuzzy control laws that human use, which are expressed in the form of rules. The application of fuzzy control systems are of great importance in industry, navigation of space vehicles, flight control, missile speed control, etc. Frequently, such systems have many variables to control and are known as complex systems. For such systems, the fuzzy rule bases exponentially explode. The hierarchical method solves this problem by considerably reducing the number of rules. However, the performance of the resulting reduced system depends on the choice of some parameters which currently are found based on the experience and knowledge of a skilled system designer. In this work, we propose a method that uses a genetic algorithm to automatically estimate the corresponding parameters for the hierarchical reduction of the rule base. The implementation process, the simulation experiments and some results are presented.

ENHANCING KAPPA NUMBER CONTROL IN DOWNFLOW LO-SOLIDSTM DIGESTER USING DIAGNOSIS AND MODELLING

Timo Ahvenlampi and Rami Rantanen

In this study, Kappa number prediction and diagnosis in continuous Downflow Lo-Solids$^{TM}$ cooking application is investigated. The Kappa number is one of the quality measures in the pulp cooking process and usually the only on-line measurement. It is a measure of the residual lignin content in the pulp. The Kappa number is mainly controlled by the cooking temperature. In this study, Kappa number control (temperature control) is carried out using Gustafson's Kappa number model for the prediction of the blowline Kappa number. New temperature set point is solved iteratively based on the difference between the predicted and target blow-line kappa numbers. The input variables are monitored using self-organizing map (SOM). The data is collected from industrial continuous Downflow Lo-Solids{TM} cooking digester. Good results were achieved using the proposed approach.

GLOBAL ASYMPTOTIC VELOCITY OBSERVATION OF NONLINEAR SYSTEMS - Application to a Frictional Industrial Emulator

R. Guerra, C. Iurian, L. Acho, F. Ikhouane and J. Rodellar

In mechanical systems with friction, development of velocity observers deserves a special emphasis because, as evidenced in numerical and experimental tests when a state-of-the-art observer is armed, friction can induce high-frequency oscillations in the estimated velocity. In this short paper, two new velocity-observation algorithms are designed, based on this previously reported observer, which eliminate the high-frequency oscillations noted in the original one. Numerical and experimental performance comparisons are carried through in a mechanical PID control system where the estimated velocity is incorporated into the feedback loop.

A MULTI AGENT CONTROLLER FOR A MOBILE ARM MANIPULATOR

Sébatien Delarue, Philippe Hoppenot and Etienne Colle

In the assistive robotics domain, and especially for disable people, the use of mobile arm manipulator can be of a great help in the everyday life tasks. First, these systems must be reliable and fault tolerant. Secondly they must facilitate man machine co-operation. This article exposes a method based on multi agent system. This kind of distributed architecture makes possible to be fault-tolerant without any specific fault management, and thus to improve reliability. It is also possible to add specific constraints, for example human like behaviors in order to facilitate the use of the system by the operator. Moreover, this method is easy to implement

A PARAMETERIZED GENETIC ALGORITHM IP CORE DESIGN AND IMPLEMENTATION

K. M. Deliparaschos, G. C. Doyamis and S. G. Tzafestas

Genetic Algorithm (GA) is a directed random search technique working on a population of solutions and based on natural selection. However, its convergence to the optimum may be very slow for complex optimization problems, especially when the GA is software implemented, making it difficult to be used in real time applications. In this paper a parameterized GA IP is designed and implemented on hardware, achieving impressive time–speedups when compared to its software version. The parameterization stands for the number of population individuals and their bit resolution, the bit resolution of each individual’s fitness, the number of elite genes in each generation, the crossover and mutation methods, the maximum number of generations, the mutation probability and its bit resolution. The proposed architecture is implemented in a field programmable gate array chip (FPGA) with the use of a very high-speed integrated circuits hardware description language (VHDL) and advanced synthesis and place and route tools. The GA discussed in this work achieves a frequency rate of 92 MHz and is evaluated using the Traveling Salesman Problem as well as several benchmarking functions.

TRACKING A WHEELCHAIR WITH A MOBILE PLATFORM

B.Allart, B. Marhic, L. Delahoche, A. Clérentin and O. Rémy-Néris

This article deals with a target tracking application for the disabled. The objective of this work is to track a wheelchair with a mobile platform and an embedded grasping arm (MANUS). We propose an approach based on an association of two Kalman filtering levels. The first level permits an estimation of the wheelchair configuration. The second is used to compute the mobile platform configuration in connection with its environment. The association of the two filtering process allows a robust tracking between two objects in movement.

ON TUNING THE DESIGN OF AN EVOLUTIONARY ALGORITHM FOR MACHINING OPTIMIZATION PROBLEMS

Jean-Louis Vigouroux, Sebti Foufou1, Laurent Deshayes, James J. Filliben, Lawrence A. Welsch and M. Alkan Donmez

In this paper, a methodology for tuning the design of an evolutionary algorithm (EA) is presented. An EA for solving machining optimization problems having highly non-linear constraints and uncertainties is studied. A conventional turning optimization problem, solved previously with classic optimization algorithms, serves as a basis for the investigation of the EA. The parameters of the problem now can be modified in a certain range, and statistical engineering methods are used to find a unique set of algorithm parameters giving robust results.

RSRT: RAPIDLY EXPLORING SORTED RANDOM TREE - Online Adapting RRT to Reduce Computational Solving Time while Motion Planning in Wide Configuration Spaces

Nicolas Jouandeau

We present a new algorithm, named RSRT, for Rapidly-exploring Random Trees(RRT) based on inherent relations analysis between RRT components. RRT algorithms are designed to consider interactions between these inherent components. We explain properties of known variations and we present some future once which are required to deal with dynamic strategies. We present experimental results for a wide set of path planning problems involving a free flying object in a static environment. The results show that our RSRT algorithm is faster than existing ones. This results can also stand as a starting point of a motion planning benchmark instances which would make easier further comparative studies of path planning algorithms.

THE VERIFICATION OF TEMPORAL KNOWLEDGE BASED SYSTEMS - A Case-study on Power-systems

Jorge Santos, Zita Vale, Carlos Ramos and Carlos Serôdio

The verification and validation (V\&V) process states if the software requirements specifications have been correctly and completely fulfilled. The methodologies proposed in software engineering showed to be inadequate for knowledge based systems (KBS) validation and verification, since KBS present some particular characteristics. Designing KBS for dynamic environments requires the consideration of temporal knowledge reasoning and representation (TRR) issues. Although humans present a natural ability to deal with knowledge about time and events, the codification and use of such knowledge in information systems still pose many problems. Hence, the development of applications strongly based on temporal reasoning remains an hard and complex task. Furthermore, albeit the last significant developments in TRR area, there is still a considerable gap for its successful use in practical applications. VERITAS is an automatic tool developed for KBS verification which is able to detect a large number of knowledge anomalies. It addresses many relevant aspects considered in real applications, like the usage of rule triggering selection mechanisms and temporal reasoning. This paper presents a solution, based in the combination of formal methods and heuristics, addressing some open issues on verification of KBS applied in critical domains.

A COMPARISON OF HUMAN AND MARKET-BASED ROBOT TASK PLANNERS

Guido Zarrella, Robert Gaimari and Bradley Goodman

Urban search and rescue, reconnaissance, manufacturing, and team sports are all problem domains requiring multiple agents that are able to collaborate intelligently to achieve a team goal. In these domains task planning and assignment can be challenging to robots and humans alike. In this paper we introduce a market-based distributed task planning algorithm that has been adapted for heterogeneous, tightly coordinated robots in domains with time deadlines. We also report the results of our experiments comparing the robots' decisions with the decisions produced by ten teams of humans performing an identical search and rescue task. The outcome provides insight into the types of problems for which information technology can add value by providing decision support for human problem solvers.

HOLONIC PRODUCTION PROCESS: A MODEL OF COMPLEX, PRECISE, AND GLOBAL SYSTEMS

Edgar Chacon, Isabel Besembel, Dulce Rivero and Juan Cardillo

The abstract should summarize the contents of the paper and should contain at least 70 and at most 200 Nowadays, it is needed a complete description of the production process in order to plan, program, control, and supervise the production process itself. The complexity to obtain this description is due to the integration of two contradictory points of views. First, the precision implicated in the construction of total and complete models, and on the other hand, the need of having a global vision associated with the different views of the process. These views normally show three important aspects: the structural organization of the model, the dynamism between the main components, and the distinct temporal scales and levels, where are taken the main decisions. The holonic approach (Erikson,2004) has been used to manage this complexity, in order to have an abstraction that permit the integration of the mentioned points of views.

CHANGE-POINT DETECTION WITH SUPERVISED LEARNING AND FEATURE SELECTION

Victor Eruhimov, Vladimir Martyanov, Eugene Tuv and George C. Runger

Data streams with high dimensions are more and more common as data sets become wider. Time segments of stable system performance are often interrupted with change events. The change-point problem is to detect such changes and identify attributes that contribute to the change. Existing methods focus on detecting a single (or few) change-point in a univariate (or low-dimensional) process. We consider the important highdimensional multivariate case with multiple change-points and without an assumed distribution. The problem is transformed to a supervised learning problem with time as the output response and the process variables as inputs. This opens the problem to a wide set of supervised learning tools. Feature selection methods are used to identify the subset of variables that change. An illustrative example illustrates the method in an important type of application.

MULTICRITERIAL DECISION-MAKING IN ROBOT SOCCER STRATEGIES

Petr Tucnık, Jan Kozany and Vilém Srovnal

The principle of multicriterial decision-making is used for the purpose of autonomous control of both individual agent and the multiagent team as a whole. This approach to the realization of control mechanism is non-standard and experimental and the robot soccer game was chosen as a testing ground for this control method. It provides an area for further study and research and some of the details of its design will be presented in this paper.

MINIMIZING THE ARM MOVEMENTS OF A MULTI-HEAD GANTRY MACHINE

Timo Knuutila, Sami Py¨otti¨al¨a and Olli S. Nevalainen

In printed circuit board (PCB) manufacturing multi-head gantry machines are becoming increasingly more popular in surface mount technology (SMT), because they combine high speed with moderate price. This kind of machine picks up several components from the feeder and places them on the PCB. The process is repeated until all component placements are done. In this article, a subproblem of the machine control is studied. Here, the placement order of the components, the nozzles in the placement arm and the component locations in the feeder are fixed. The goal is to find an optimal pick-up sequence when minimizing the total length of the arm movements. An algorithm that searches the optimal pick-up sequence is proposed and tested widely. Tests show that the method can be applied to problems of practical size.

A GROWING FUNCTIONAL MODULE DESIGNED TO TRIGGER CAUSAL INFERENCE

Jérôme Leboeuf Pasquier

“Growing Functional Modules” constitutes a prospective paradigm founded on the epigenetic approach whose proposal consists in designing a distributed architecture, based on interconnected modules, that allows the automatic generation of an autonomous and adaptive controller (artificial brain). The present paper introduces a new module designed to trigger causal inference; its functionality is discussed and its behavior is illustrated applying the module to solve the problem of a dynamic maze.

A MULTI CRITERIA EVALUATION OVER A FINITE SCALE FOR MAINTENANCE ACTIVITIES OF A MOTORWAY OPERATOR

Céline Sanchez, Jacky Montmain, Marc Vinches and Brigitte Mahieu

The Escota Company aims at the formalization and improvement of the decisional process for preventive maintenance in a multi criteria (MC) environment. According to available pieces of knowledge on the infrastructure condition, operations are to be evaluated with regards to (w.r.t.) technical but also to conformity, security and financial criteria. This MC evaluation is modelled as the aggregation of partial scores attributed to an operation w.r.t. a given set of n criteria. The scores are expressed over a finite scale which can cause some troubles when no attention is paid to the aggregation procedure. This paper deals with the consistency of the evaluation process, where scores are expressed as labels by Escota’s experts, whereas the aggregation model is supposed to deal with numerical values and cardinal scales. We try to analyse this curious but common apparent paradox in MC evaluation when engineering contexts are concerned. A robustness study of the evaluation process concludes this paper.

COGNITIVE APPROACH TO PROBLEM SOLVING OF SOCIAL AND ECONOMIC OBJECT DEVELOPMENT

Z. Avdeeva, S. Kovriga and D. Makarenko

The basic technique of problem-solving is structurization of knowledge about object and its environment and construction of a cognitive model. The technique includes monitoring of dynamics of factors of the model (their tendencies), analysis of the model structure with the use of SWOT-approach, and modeling that permits to determine and solve semi-structured problems. The technique allows supporting of a vital control task that consists in goal setting of socio-economic object development, as far as solution of discovered problems turns into the system development control task. The application of technique is useful when designing a strategy of development of social and economic objects.

FEASIBILITY OF SUBSPACE IDENTIFICATION FOR BIPEDS - An Innovative Approach for Kino-Dynamic Systems

Muhammad Saad Saleem and Ibrahim A. Sultan

Different approaches have been overviewed which have been used in stability of biped robots. Current implementations either mimic human behavior or use heuristic control. This paper suggests the use of supervisory crisp control in operational space configuration for better control and understanding of kino-dynamic systems and biped robots.

IDENTIFICATION OF MODELS OF EXTERNAL LOADS

Yuri Menshikov

In the given work the problem of construction (synthesis) of mathematical model of unknown or little-known external load (EL) on open dynamic system is considered. Such synthesis is carried out by special processing of the experimentally measured response of dynamic system on researched real external load and known external loads (method of identification). This problem is considered in two statements: the synthesis of EL for certain model and the synthesis of EL for models class for the purposes of mathematical modeling. These problems are ill-posed by their nature and so the method of Tikhonov's regularization is used for its solution. For increase of exactness of problem solution of synthesis for models class the method of choice of special mathematical models (MM) is used. The calculation of model of external load for rolling mills is executed.

USEFUL COMPUTER VISION TECHNIQUES FOR A ROBOTIC HEAD

O. Deniz, M. Castrillon, J. Lorenzo and L. A. Canalis

This paper describes some simple but useful computer vision techniques for human-robot interaction. First, an omnidirectional camera setting is described that can detect people in the surroundings of the robot, giving their angular positions and a rough estimate of the distance. The device can be easily built with inexpensive components. Second, we comment on a color-based face detection technique that can alleviate skin-color false positives. Third, a person tracking and recognition system is described. Finally, a simple head nod and shake detector is described, suitable for detecting affirmative/negative, approval/disapproval, understanding/disbelief head gestures.

FUZZY-SYNTACTIC APPROACH TO PATTERN RECOGNITION AND SCENE ANALYSIS

Marzena Bielecka, Marek Skomorowski and Andrzej Bielecki

In syntactic pattern recognition an object is described by symbolic data. The problem of recognition is to determine whether the describing mathematical structure, for instance a graph, belongs to the language generated by a grammar describing the mentioned mathematical structures. So called ETPL(k) graph grammars are a known class of grammars used in pattern recognition. The approach in which ETPL(k) grammars are used was generalized by using probabilistic mechanisms in order to apply the method to recognize distorted patterns. In this paper the next step of the method generalization is proposed. The ETPL(k) grammars are improved by fuzzy sets theory. It turns out that the mentioned probabilistic approach can be regarded as a special case of the proposed one. Applications to robotics are considered as well.

TASK PLANNER FOR HUMAN-ROBOT INTERACTION INSIDE A COOPERATIVE DISASSEMBLY ROBOTIC SYSTEM

Carolina Díaz, Santiago Puente and Fernando Torres

This paper develops a task planner that allows including a human operator which works cooperatively with robots inside an automatic disassembling cell. This method gives the necessary information to the system and the steps to be followed by the manipulator and the human, in order to obtain an optimal disassembly and a free-shock task assignation that guarantees the safety of the human operator.

A SET APPROACH TO THE SIMULTANEOUS LOCALIZATION AND MAP BUILDING - Application to Underwater Robots

Luc Jaulin, Frédéric Dabe, Alain Bertholom and Michel Legris

This paper proposes a set approach for the simultaneous localization and mapping (SLAM) in a submarine context. It shows that this problem can be cast into a constraint satisfaction problem which can be solve efficiently using interval analysis and propagation algorithms. The efficiency of the resulting propagation method is illustrated on the localization of submarine robot, named Redermor. The experiments have been collected by the GESMA (Groupe d'Etude Sous-Marine de l'Atlantique) in the Douarnenez Bay, in Brittany.

ROBUST AND ACTIVE TRAJECTORY TRACKING FOR AN AUTONOMOUS HELICOPTER UNDER WIND GUST

Adnan Martini, François Léonard and Gabriel Abba

The helicopter manoeuvres naturally in an environment where the execution of the task can easily be affected by atmospheric turbulences, which lead to variations of its model parameters. Here, a nonlinear simple model with 3-DOF of a helicopter with unknown disturbances is used. Two approaches of robust control are compared via simulations: a nonlinear feedback and an active disturbance rejection control based on a nonlinear extended state observer(ADRC).

A NEWMARK FMD SUB-CYCING ALGORITHM

J. C. Miao, P. Zhu, G. L. Shi and G. L. Chen

The sub-cycling algorithm, which was firstly presented by Belytschko T. et al [5], have been successfully applied in structural dynamical FEM analysis. However, sub-cycling algorithms, which can be used for the flexible multi-body systems (FMS), are still not presented up to now. This paper presents a Newmark method based sub-cycling algorithm, which is suitable for solving the condensed flexible multi-body dynamic (FMD) equations. Common-step update formations and sub-step update formations for quickly changing variables and slowly changing variables of the FMD formations are established based on the original differential equations. Stability of the sub-cycling procedure is checked by means of energy balance checking during the integral process. Numerical examples indicate that the sub-cycling algorithm is able to enhance the computational efficiency without dropping results accuracy greatly.

RPQ: ROBOTIC PROXIMITY QUERIES - Development and Applications

Albert Hernansanz, Xavier Giralt, Alberto Rodriguez and Josep Amat

This paper presents a robotic proximity query package (RPQ) as an optimization of the general collision library PQP (Proximity Query Package) for the detection of collisions and distance computation between open kinematic chains such as robotic arms. The performance of the optimizations to non specific collision query packages are explained and evaluated. Finally, a robotic assisted surgical application is presented which has been used as a test bed for the proximity package.

ESTIMATION OF CAMERA 3D-POSITION TO MINIMIZE OCCLUSIONS

Pablo Gil, Fernando Torres and Oscar Reinoso

Occlusions are almost always seen as undesirable singularities that pose difficult challenges to recognition processes of objects which have to be manipulated by a robot. Often, the occlusions are perceived because the point of view with which a scene is observed is not adapted. In this paper, a strategy to determine the location, orientation and position, more suitable so that a camera has the best point of view to capture a scene composed by several objects is presented. The estimation for the best location of the camera is based on minimizing the zones of occlusion by the analysis of a virtual image sequence in which is represented the virtual projection of the objects. These virtual projections represent the images as if they were captured by a camera with different view points without moving it

WEB-BASED INTERACTIVE POSITIONING CONTROL OF AN ELECTRIC FLATCAR VIA WIRELESS NETWORK

Ken Ishii, Koki Abe and Yoshimi Takao

A large tank has been used for target strength pattern measurements of fish. It is equipped with an electric flatcar. Further an elevation-rotating unit runs on the rails above it. The probe on the top of its elevation unit is equipped with an attachment for an ultrasonic transducer. The manipulator probe is movable in the four directions of the x, y, z and rotation axes. Installation of a remote control switch has been required for the purpose of efficient operation of an electric flatcar. A remote control system using a notebook personal computer has been developed with good cost performance. The PC is equipped with a wireless network interface card. A model of WEB direct-access monitoring has been designed newly on the basis of the concept that an operator can control a mechanical device using a WEB Browser via LAN. Furthermore it performs double exclusive control for access from multi PCs, and has made possible a controller and multiple-monitor system. The mission was performed for the purpose of evaluation of WEB operation. The result has made clear the specifications for motion, and an external interface of the electric flatcar is applicable to the new protocol developed for WEB Browser control.

ADAPTIVE CONTROL BY NEURO-FUZZY SYSTEM OF AN OMNI-DIRECTIONAL WHEELCHAIR USING A TOUCH PANEL AS HUMAN-FRIENDLY INTERFACE

Kazuhiko Terashima, Yoshiyuki Noda, Juan Urbano, Sou Kitamura, Takanori Miyoshi and Hideo Kitagawa

For improving the operability of an omni-directional wheelchair provided with a power assist system, the system must be able to adapt to the individual characteristics of the many different attendants that will use it. For achieving this purpose, an innovative human-interface using a touch panel that provides easy input and feedback information in real time of the operation of a power-assisted wheelchair was developed. The system was tested experimentally with many different attendants and the results show that in addition to providing a human friendly interface by using the touch panel system with monitor it can adapt successfully to the particular habits of the attendants.

NAVIGATION SYSTEM FOR INDOOR MOBILE ROBOTS BASED ON RFID TAGS

Toshifumi Tsukiyama and Atsushi Suzuki

A new navigation method is described for an indoor mobile robot. The robot system is composed of a Radio Frequency Identification (RFID) tag sensor and a commercial three-wheel mobile platform with ultrasonic rangefinders. The RFID tags are used as landmarks for navigation and the topological relation map which shows the connection of scattered tags through the environment is used as course instructions to a goal. The robot automatically follows paths using the ultrasonic rangefinders until a tag is found and then refers the next movement to the topological map for a decision. Our proposed technique would be useful for real-world robotic applications such as intelligent navigation for motorized wheelchairs.

IMPROVEMENT OF THE VISUAL SERVOING TASK WITH A NEW TRAJECTORY PREDICTOR - The Fuzzy Kalman Filter

C. Pérez, N. García, J. M. Sabater, J. M. Azorín, O. Reinoso and L. Gracia

Visual Servoing is an important issue in robotic vision but one of the main problems is to cope with the delay introduced by acquisition and image processing. This delay is the reason for the limited velocity and acceleration of tracking systems. The use of predictive techniques is one of the solutions to solve this problem. In this paper, we present a Fuzzy predictor. This predictor decreases the tracking error compared with the classic Kalman filter (KF) for abrupt changes of direction and can be used for an unknown object's dynamics. The Fuzzy predictor proposed in this work is based on several cases of the Kalman filtering, therefore, we have named it: Fuzzy Kalman Filter (FKF). The robustness and feasibility of the proposed algorithm is validated by a great number of experiments and is compared with other robust methods.

EVOLUTIONARY PATH PLANNING FOR UNMANNED AERIAL VEHICLES COOPERATION

Ioannis K. Nikolos and Nikos Tsourveloudis

We suggest an evolutionary based off-line/on-line path planner for cooperating Unmanned Aerial Vehicles (UAVs) that takes into account the environment characteristics and the flight envelope and mission constraints of the cooperating UAVs. The scenario under consideration is the following: a number of UAVs are launched from the same or different known initial locations. The main issue is to produce 3-D trajectories that ensure a collision free operation with respect to mission constraints. The path planner produces curved routes that are represented by 3-D B-Spline curves. Two types of planner are discussed: The off-line planner generates collision free paths in environments with known characteristics and flight restrictions. The on-line planner, which is based on the off-line one, generates collision free paths in unknown static environments, by using acquired information from the UAV’s on-board sensors. This information is exchanged between the cooperating UAVs in order to maximize the knowledge of the environment. Both off-line and on-line path planning problems are formulated as optimization problems, with a Differential Evolution algorithm to serve as the optimizer.

VISUAL ALIGNMENT ROBOT SYSTEM: KINEMATICS, PATTERN RECOGNITION, AND CONTROL

SangJoo Kwon and Chansik Park

The visual alignment robot system for display and semiconductor fabrication process largely consists of multi-axes precision stage and vision peripherals. One of the central issues in a display or semiconductor mass production line is how to reduce the overall tact time by making a progress in the alignment technology between mask and panel. In this paper, we suggest the kinematics of the 4PPR parallel alignment mechanism with four limbs unlike usual three limbs cases and an effective pattern recognition algorithm for alignment mark recognition. The inverse kinematic solution determines the moving distances of joint actuators for an identified mask-panel misalignment. Also, the proposed alignment mark detection method enables considerable reduction in computation time comparing with well-known pattern matching algorithms.

INTEGRATED DESIGN OF A MECHATRONIC SYSTEM - The Pressure Control in Common Rails

Paolo Lino and Bruno Maione

This paper describes the integrated design of the pressure control in a common-rail injection system. Mechanical elements and the embedded controller are considered as a whole, using a multi-disciplinary approach to modelling and simulation. The virtual prototype, which provides the detailed geometrical/physical model of the mechanical parts, plays the role of a surrogate of a reference hardware prototype in reduced-order modelling, validation, and/or in tuning the control parameters. The results obtained by the proposed approach are compared and validated by experiments.

ULTRA VIOLET IMAGING TRANSDUCER CONTROL OF A THERMAL SPRAYING ROBOT

D. Breen, E. Coyle and D. M. Kennedy

The thermal spraying industry has a global market of $1.3 billion. This industry relies heavily on manual operation of the thermal spraying equipment or in some cases, robotic systems that require costly set up of material for surface coating and time consuming trajectory planning. The main objective of this research was to investigate novel ideas for automating the thermal spraying process. This requires transducers that can provide information about arbitrarily shaped and orientated material for spraying and generating the trajectory plan for the robot manipulator during the thermal spraying process in real time. The most significant difficulty for any transducer, particularly low cost vision systems is the thermal spraying process which in our research is molten material such as aluminium in an Oxy-Acetylene flame with temperatures exceeding 31000C. This paper outlines the concept and based on the experimental results presented demonstrates combined optical and image processing techniques for obtaining information about objects behind a butane flame.

ON COMPUTING MULTI-FINGER FORCE-CLOSURE GRASPS OF 2D OBJECTS

Belkacem Bounab, Daniel Sidobre and Abdelouhab Zaatri

In this paper, we present a new necessary and sufficient force-closure condition for multi-finger two-dimensional (2D) grasps. Assuming hard-finger point contact under Coulomb friction model, we develop a new algorithm for computing force-closure grasps of 2D objects using multifingred hand. Based on the central axis theory, an easily computable algorithm for force-closure grasps has been implemented and its efficiency has been demonstrated by examples.

FAST COMPUTATION OF ENTROPIES AND MUTUAL INFORMATION FOR MULTISPECTRAL IMAGES

Sié Ouattara, Alain Clément and François Chapeau-Blondeau

This paper describes the fast computation, and some applications, of entropies and mutual information for color and multispectral images. It is based on the compact coding and fast processing of multidimensional histograms for digital images.

COMPARISON OF TWO IDENTIFICATION TECHNIQUES: THEORY AND APPLICATION

Pierre-Olivier Vandanjon, Alexandre Janot, Maxime Gautier and Flavia Khatounian

Parametric identification requires a well know-how and an accurate analysis. The most popular methods consist in using simply the least squares techniques because of their simplicity. However, these techniques are not intrinsically robust. An alternative consists in helping them with an appropriate data treatment. Another choice consists in applying a robust identification method. This paper focuses on a comparison of two techniques: a “helped” least squares technique and a robust method called “the simple refined instrumental variable method”. These methods will be applied to a single degree of freedom haptic interface.

VEHICULAR ELECTRONIC DEVICES CONNECTED BY ONBOARD FIELDBUS TECHNOLOGIES

Miguel A. Domínguez, Perfecto Mariño, Francisco Poza and Santiago Otero

The electrical circuits and their Electronic Control Units (ECUs) in buses and coaches are essential for their good working. Drive, braking, suspension, opening door, security and communication devices must be integrated in a reliable and real time information system. The industrial communication networks or fieldbuses are a good solution to implement networked control systems for the onboard electronics in the public transport buses and coaches. The authors are working in the design of multiplexed solutions based on fieldbuses to integrate the body and chassis functions of city public transport buses. An example for the EURO5 model of the Scania manufacturer is reported in this paper. The authors are also working in the implementation of new modules based on FPGAs (Field Programmable Gate Arrays) that can be used in these networked control systems.

SELECTIVE IMAGE DIFFUSION FOR ORIENTED PATTERN EXTRACTION

A. Histace, V. Courboulay and M. Ménard

In this short paper, we present an oriented pattern extraction method for image processing. This method is based on the use of a particular Partial Differential Equation (PDE), issued from information theory, which makes it possible the integration, within the diffusion process, of prior knowledge on the data to extract. We show, as a proof of feasibility, that this method makes it possible the integration of selectiveness during the restoration process. This method may nd applicability in industrial quality checks or vision in robotics for example.

ESTIMATION OF STATE AND PARAMETERS OF TRAFFIC SYSTEM

Pavla Pecherková, Jitka Homolová and Jindrich Duník

This paper deals with the problem of modelling and estimation of traffic model state and parameters for old urban areas. The most important properties of the traffic system are described. Then the model of the traffic system is recalled. The weakness of the model is pointed out and subsequently rectified. Various estimation and identification techniques, used in the traffic problem, are introduced. The performance of various filters is validated, using the derived model and synthetic and real data coming from the centre of Prague, with respect to filter accuracy and complexity.

ADDRESSING COMPLEXITY ISSUES IN A REAL-TIME PARTICLE FILTER FOR ROBOT LOCALIZATION

Dario Lodi Rizzini, Francesco Monica, Stefano Caselli and Monica Reggiani

Exploiting a particle filter for robot localization requires expensive filter computations to be performed at the rate of incoming sensor data. These high computational requirements prevent exploitation of advanced localization techniques in many robot navigation settings. The Real-Time Particle Filter (RTPF) provides a tradeoff between sensor management and filter performance by adopting a mixture representation for the set of samples. In this paper, we propose two main improvements in the design of a RTPF for robot localization. First, we describe a novel solution for computing mixture parameters relying on the notion of effective sample size. Second, we illustrate a library for RTPF design based on generic programming and providing both flexibility in the customization of RTPF modules and efficiency in filter computation. In the paper, we also report results comparing the localization performance of the proposed extension and of the original RTPF algorithm.

PC-SLIDING FOR VEHICLES PATH PLANNING AND CONTROL - Design and Evaluation of Robustness to Parameters Change and Measurement Uncertainty

Mariolino De Cecco, Enrico Bertolazzi, Giordano Miori, Roberto Oboe and Luca Baglivo

A novel technique called PC-Sliding for path planning and control of non-holonomic vehicles is presented and its performances analysed in terms of robustness. The path following is based upon a polynomial curvature planning and a control strategy that replans iteratively to force the vehicle to correct for deviations while sliding over the desired path. Advantages of the proposed method are its logical simplicity, compatibility with respect to kinematics and partially to dynamics. Chained form transformations are not involved. Resulting trajectories are convenient to manipulate and execute in vehicle controllers while computed with a straightforward numerical procedure in real-time. The performances of the method that embody a planner, a controller and a sensor fusion strategy is verified by Monte Carlo method to assess its robustness to parameters changes and measurement uncertainties.

PROPERTY SERVICE ARCHITECTURE FOR DISTRIBUTED ROBOTIC AND SENSOR SYSTEMS

Antti Tikanmäki and Juha Röning

This paper presents a general architecture for creating complex distributed software systems, called Property Service architecture. The system may contain resources like robots, sensors, and different kinds of system services, such as controller units, data storages, or a collective model of the environment. This architecture contains several solutions and distributed system design methods for developing advanced and complex systems. It also provides the possibility to add new resources to the system easily and during operation. Each service has a very simple generalized interface. This meets the requirements of distributed robotic applications, such as remote operation, multi-robot cooperation, and the robot’s internal operation. The simplicity of the interface also provides a possibility to scale down the service even on the low-cost, low-performance microcontrollers used in small-sized robots. The main features of the architecture are the dynamic properties of the resources, automatic reconfiguration, and the high level of reusability of the implemented methods and algorithms.

DEVELOPMENT OF THE CONNECTED CRAWLER ROBOT FOR ROUGH TERRAIN - Realization of the Autonomous Motions

Sho Yokota, Yasuhiro Ohyama, Hiroshi Hashimoto, Jin-Hua She, Kuniaki Kawabata, Pierre Blazevic and Hisato Kobayashi

The purpose of this paper is to develop the mobile system for rough terrain. Our mobile system adopts the connected crawler mechanism. This mechanism had 3 connected stages with the motor-driven crawler tracks on each side. RC-servo motors were used for driving joints between the stages. This system also have a high mobility on rough terrain. In this paper, we show the mechanical features, and propose the operation strategies for autonomous motions. We have also made verification experiment of proposed operation strategies. For this verification, we prepared 2 kinds of experiment. One was that the robot passes over the different heihgt of bumps. The other was stairs ascending. Both experiments had a great success. There were remarkable points in these experiments. These experiment showed that the robot can pass over the different height and different structures obstacles by using only (same) strategies. Moreover the sensors which realize proposed strategies was very simple, and the number of sensor was very small. Therefore it can be concluded that proposed strategies has extremely high usefulness.

ESTIMATION PROCESS FOR TIRE-ROAD FORCES AND VEHICLE SIDESLIP ANGLE

Guillaume Baffet, Ali Charara and Daniel Lechner

This study focuses on the estimation of car dynamic variables for the improvement of vehicle safety, handling characteristics and comfort. More specifically, a new estimation process is proposed to estimate longitudinal/lateral tire-road forces, velocity, sideslip angle and wheel cornering stiffness. This method uses measurements from currently-available standard sensors (yaw rate, longitudinal/lateral accelerations, steering angle and angular wheel velocities). The estimation process is separated into two blocks: the first block contains an observer whose principal role is to calculate tire-road forces without a descriptive force model, while in the second block an observer estimates sideslip angle and cornering stiffness with an adaptive tire-force model. The different observers are based on an Extended Kalman Filter (EKF). The estimation process is applied and compared to real experimental data, notably sideslip angle and wheel force measurements. Experimental results show the accuracy and potential of the estimation process.

APPLICATION OF A HUMAN FACTOR GUIDELINE TO SUPERVISORY CONTROL INTERFACE IMPROVEMENT

Pere Ponsa, Ramon Vilanova, Marta Díaz and Anton Gomà

In tasks of human supervision in industrial control room they are applied generic disciplines as the software engineering for the design of the computing interface and the human factors for the design of the control room layout. From the point of view of the human computer interaction, to these disciplines it is necessary to add the usability engineering and the cognitive ergonomics since they contribute rules for the user centered design. The main goal of this work is the application of a human factors guideline for supervisory control interface design in order to improve the efficiency of the human machine systems in automation. This communication presents the work developed to improve the Sports Service Area interface of the Universitat Autónoma de Barcelona.

SEGMENTATION OF SATELLITE IMAGES IN OPTOELECTRONIC SYSTEM

Andrey S. Ostrovsky, Ernesto Pino-Mota and Paulo C. Romero-Soría

The problem of segmenting the satellite images into homogeneous texture regions that correspond to the different classes of terrestrial surface is considered. It is shown that this problem may be successfully solved by using the method of spectral synthetic discriminant functions recently proposed by the authors for classification of random image fields and realized by means of a rather simple optoelectronic technique. The experimental results of segmenting the true satellite images are given.

COMPARING COMBINATIONS OF FEATURE REGIONS FOR PANORAMIC VSLAM

Arnau Ramisa, Ramón López de Mántaras, David Aldavert and Ricardo Toledo

Invariant (or covariant) image feature region detectors and descriptors are useful in visual robot navigation because they provide a fast and reliable way to extract relevant and discriminative information from an image and, at the same time, avoid the problems of changes in illumination or in point of view. Furthermore, complementary types of image features can be used simultaneously to extract even more information. However, this advantage always entails the cost of more processing time and sometimes, if not used wisely, the performance can be even worse. In this paper we present the results of a comparison between some of these combinations. The test performed consists in computing the essential matrix between panoramic images using correspondences established with these methods. Different combinations of region detectors and descriptors are evaluated and validated using ground truth data. The results will help us to find the best combination to use it in a robot navigation system.

TRAJECTORY PLANNING USING OSCILLATORY CHIRP FUNCTIONS APPLIED TO BIPEDAL LOCOMOTION

Fernando Juan Berenguer Císcar and Félix Monasterio-Huelin Maciá

This work presents a method for planning sinusoidal trajectories for an actuated joint, so that the oscillation frequency follows linear profiles, like trapezoidal ones, defined by the user or by a high level planner. The planning method adds a cubic polynomial function for the last segment of the trajectory in order to reach a desired final position of the joint. We apply this planning method to an underactuated bipedal mechanism which gait is generated by the oscillatory movement of its tail. Using linear frequency profiles allow us to modify the speed of the mechanism and to study the efficiency of the system at different speed values.

EXTENSION OF THE GENERALIZED IMAGE RECTIFICATION - Catching the Infinity Cases

Klaus Häming and Gabriele Peters

This paper addresses the topic of image rectification, a widely used technique in 3D-reconstruction and stereo vision. The most popular algorithm uses a projective transformation to map the epipoles of the images to infinity. This algorithm fails whenever an epipole lies inside an image. To overcome this drawback, a rectification scheme known as polar rectification can be used. This, however, fails whenever an epipole lies at infinity. For autonomous systems exploring their environment, it can happen that successive camera positions constitute cases where we have an image pair with one epipole at infinity and the other inside an image. So neither of the previous algorithms can be applied directly. We present an extension to the polar rectification scheme. This extension allows the rectification of image pairs whose epipoles lie even at such difficult positions. Additionally, we discuss the necessary computation of the orientation of the epipolar geometry in terms of the fundamental matrix directly, avoiding the computation of a line homography as in the original polar rectification process.

MOTION CONTROL OF AN OMNIDIRECTIONAL MOBILE ROBOT

Xiang Li and Andreas Zell

This paper focuses on the motion control problem of an omnidirectional mobile robot. A new control method based on the inverse input-output linearized kinematic model is proposed. As the actuator saturation and actuator dynamics have important impacts on the robot performance, this control law takes into account these two aspects and guarantees the stability of the closed-loop control system. Real-world experiments with an omnidirectional middle-size RoboCup robot verifies the performance of this proposed control algorithm.

DYNAMIC REAL-TIME REDUCTION OF MAPPED FEATURES IN A 3D POINT CLOUD

Marko Reimer and Bernardo Wagner

This paper presents a method to reduce the data collected by a 3D laser range sensor. The complete point cloud consisting of several thousand points is hard to process on-line and in real-time on a robot. Similar to navigation tasks, the reduction of these points to a meaningful set is needed for further processes of object recognition. This method combines the data from a 3D laser sensor with an existing 2D map in order to reduce mapped feature points from the raw data. The main problem is the computational complexity of considering the different noise sources. The functionality of our approach is demonstrated by experiments for on-line reduction of the 3D data in indoor and outdoor environments.

MAKING SENSOR NETWORKS INTELLIGENT

Peter Sapaty, Masanori Sugisaka and Joaquim Filipe

A universal solution for management of dynamic sensor networks will be presented, covering both networking and application layers. A network of intelligent modules, overlaying the sensor network, collectively interprets mission scenarios in a special high-level language, which can start from any nodes and cover the network at runtime. The spreading scenarios are extremely compact, which may be useful for energy saving communications. The code will be exhibited for distributed collection and fusion of sensor data, also for tracking mobile targets by scattered and communicating sensors.

DATA INTEGRATION SOLUTION FOR PAPER INDUSTRY - A Semantic Storing Browsing and Annotation Mechanism for Online Fault Data

Sergiy Nikitin, Vagan Terziyan and Jouni Pyötsiä

There is a lot of IT solutions exist for simplification and time saving of industrial experts’ activities, however, due to large diversity of tools and case-by-case software development strategy, big industrial companies are looking for an efficient and viable information integration solution. The companies have realized the need for an integral environment, where information is ready for extraction and sophisticated querying. We present here a semantic web-based solution for logging and annotation of online fault data, which is designed, implemented and deployed for a particular business case of a leading paper machinery maintenance and automation company. We analyse further evolution of the system and introduce a notion of Ubiware – a generic platform for modelling of dynamic and changing environments consisting of autonomous self-configurable elements.

OPTIMAL NONLINEAR IMAGE DENOISING METHODS IN HEAVY-TAILED NOISE ENVIRONMENTS

Hee-il Hahn

The statistics for the neighbor differences between the particular pixels and their neighbors are introduced. They are incorporated into the filter to enhance images contaminated by additive Gaussian and impulsive noise. The derived denoising method corresponds to the maximum likelihood estimator for the heavy-tailed Gaussian distribution. The error norm corresponding to our estimator from the robust statistics is equivalent to Huber’s minimax norm. This estimator is also optimal in the respect of maximizing the efficacy under the above noise environment. It is mixed with the myriad filter to propose an amplitude-limited myriad filter. In order to reduce visually grainy output due to impulsive noise, Impulse-like signal detection is introduced so that it can be processed in different manner from the remaining pixels. Our approaches effectively remove both Gaussian and impulsive noise, not blurring edges severely.

COMPARISON OF FINE NEEDLE BIOPSY CYTOLOGICAL IMAGE SEGMENTATION METHODS

Maciej Hrebien, Piotr Stec and Józef Korbicz

The paper describes an early stage of cytological image recognition and presents a comparision of two hybrid segmentation methods. The analysis includes the Hough transform with conjunction to the watershed algorithm and with conjunction to the active contours techniques. One can also find here a short description of image pre-processing and an automatic nucleuses localization mechanisms used in our approach. Preliminary experimental results collected on a hand-prepared benchmark database are also presented with short discussion of common errors and possible future problems.

SMARTMOBILE – AN ENVIRONMENT FOR GUARANTEED MULTIBODY MODELING AND SIMULATION

Ekaterina Auer and Wolfram Luther

Multibody modeling and simulation is important in many areas of our life from a computer game to space exploration. To automatize the process for industry and research, a lot of tools were developed, among which the program {\sc MOBILE} plays a considerable role. However, such tools cannot guarantee the correctness of results, for example, due to possible errors in the underlying finite precision arithmetic. To avoid such errors and simultaneously prove the correctness of results, a number of so called validated methods were developed, which include interval, affine and Taylor form based arithmetics. In this paper, we present the recently developed multibody modeling and simulation tool {\sc SmartMOBILE} based on {\sc MOBILE}, which is able to guarantee the correctness of results. The use of validated methods there allows us additionally to take into account the uncertainty in measurements and study its influence on simulation. We demonstrate the main concepts and usage with the help of several mechanical systems, for which kinematical or dynamic behavior is simulated in a validated way.

BLENDING TOOL PATHS FOR G1-CONTINUITY IN ROBOTIC FRICTION STIR WELDING

Mikael Soron and Ivan Kalaykov

In certain robot applications, path planning has to be viewed, not only from a motion perspective, but also from a process perspective. In 3-dimensional Friction Stir Welding (FSW) a properly planned path is essential for the outcome of the process, even though different control loops compensate for deviations. One such example is how sharp path intersection is handled, which is the emphasis in this paper. We propose a strategy based on Hermite and Bezier curves, by which G1-continuity is obtained. The blending operation includes an optimization strategy in order to avoid high second order derivatives of the blending polynomials, yet still to cover as mush as possible of the original path.

AUTOMATIC VISION-BASED MONITORING OF THE SPACECRAFT ATV RENDEZVOUS / SEPARATIONS WITH THE INTERNATIONAL SPACE STATION

A. A. Boguslavsky, V. V. Sazonov, S. M. Sokolov, A. A. Boguslavsky, V. V. Sazonov and S. M. Sokolov

The system which allows automating the visual monitoring of the spacecraft ATV rendezvous / separations with the international space station is being considered. The initial data for this complex is the video signal received from the TV-camera, mounted on the station board. The offered algorithms of this video signal processing in real time allow restoring the basic characteristics of the spacecraft motion with respect to the international space station. The results of the experiments with the described software and real and virtual video data about the docking approach of the spacecraft with the International Space Station are being presented. The accuracy of the estimation of the motion characteristics and perspectives of the use of the package are being discussed.

CALIBRATION OF QUASI-ISOTROPIC PARALLEL KINEMATIC MACHINES: ORTHOGLIDE

Anatoly Pashkevich, Roman Gomolitsky, Philippe Wenger and Damien Chablat

The paper proposes a novel approach for the geometrical model calibration of the quasi-isotropic parallel kinematic mechanisms of the Orthoglide family. It is based on observations of the manipulator leg parallelism during motions between the specific test postures and employs a low-cost measuring system composed of standard comparator indicators attached to the universal magnetic stands. They are sequentially used for measuring the deviation of the relevant leg location while the manipulator moves the TCP along the Cartesian axes. Using the measured differences, the developed algorithm estimates the joint offsets and the leg lengths that are treated as the most essential parameters. Validity of the proposed calibration technique is confirmed by the experimental results.

DIRECTIONAL MANIPULABILITY FOR MOTION COORDINATION OF AN ASSISTIVE MOBILE ARM

K. Nait-Chabane, P. Hoppenot and E. Colle

In this paper, we address the problem of coordinated motion control of a manipulator arm embarked on a mobile platform. The mobile manipulator is used in providing assistance for disabled people. In order to perform a given task by using mobile manipulator redundancy, we propose a new manipulability measure that incorporates both arm manipulation capacities and the end-effector imposed task. This proposed measure is used in a numerical algorithm to solve system redundancy and then compared with other existing measures. Simulation and real results show the benefit and efficiency of this measure in the field of motion coordination.

IRREVERSIBILITY MODELING APPLIED TO THE CONTROL OF COMPLEX ROBOTIC DRIVE CHAINS

Omar Al Assad, Emmanuel Godoy and Vincent Croulard

The phenomena of static and dry friction may lead to difficult problems during low speed motion (e.g. stick slip phenomenon). However, they can be used to obtain irreversible mechanical transmissions. The latter tend to be very hard to model theoretically. In this paper, we propose a pragmatic approach to model irreversibility in robotic drive chains. The proposed methodology consists of using a state machine to describe the functional state of the transmission. After that, for each state we define the efficiency coefficient of the drive chain. This technique gives conclusive results during experimental validation and allows to reproduce a reliable robot simulator. This simulator is set up for the purpose of position control of a medical positioning robot.

A ROBOTIC PLATFORM FOR AUTONOMY STUDIES

Sergio Ribeiro Augusto and Ademar Ferreira

This paper describes a mobile robotic platform and a software framework for applications and development of robotic experiments integrating teleoperation and autonomy. An application using supervised learning is developed in which the agent is trained by teleoperation. This allows the agent to learn the perception to action mapping from the teleoperator in real time, such that the task can be repeated in an autonomous way, with some generalization. To make the mapping, a radial basis function network (RBF) trained with a sequential learning algorithm is used. Experimental results are shown.

INITIAL DEVELOPMENT OF HABLA (HARDWARE ABSTRACTION LAYER) - A Middleware Software Tool

Andrés Faíña, Francisco Bellas and Richard J. Duro

In this work we present the initial implementation of a middleware software tool called the Hardware Abstraction Layer (HABLA). This tool isolates the control architecture of an autonomous computational system, like a robot or an “intelligent” room, from its particular hardware implementation. It is provided with a set of general sensors and typical sensorial processing mechanisms of this kind of autonomous systems allowing for its application to different commercial platforms. This way, the HABLA permits the control designer to focus its work on higher-level tasks minimizing the time spent on the adaptation of the control architecture to different hardware configurations. Another important feature of the HABLA is that both hardware-HABLA and HABLA-control communications take place through TCP sockets, permitting the distribution of the computational cost over different computers. In addition, it has been developed in JAVA, so it is platform independent. After presenting the general HABLA diagram and operation structure, we consider a real application using the same deliberative control architecture on two different autonomous robots: an Aibo legged robot and a Pioneer 2Dx wheeled robot. Finally, the HABLA capabilities are considered in the framework of an “intelligent” environment control system.

MULTI-RESOLUTION BLOCK MATCHING MOTION ESTIMATION WITH DEFORMATION HANDLING USING GENETIC ALGORITHMS FOR OBJECT TRACKING APPLICATIONS

Harish Bhaskar and Helmut Bez

Motion Estimation is a popular technique for computing the displacement vectors between objects or attributes between images captured at subsequent time stamps. Block matching is a well known technique of motion estimation that has been successfully applied to several applications such as video coding, compression and object tracking. One of the major limitations of the algorithm is its ability to cope with deformation of objects or image attributes within the image. In this paper we present a novel scheme for block matching that combines genetic algorithms with affine transformations to accurate match blocks. The model is adapted into a multi-resolution framework and is applied to object tracking. A detailed analysis of the model alongside critical results illustrating its performance on several synthetic and real-time datasets is presented.

A NOVEL BLOCK MOTION ESTIMATION MODEL FOR VIDEO STABILIZATION APPLICATIONS

Harish Bhaskar and Helmut Bez

With the increased use of hand-held or head-mounted video capture devices for motion capture, the requirement for robust and reliable video enhancement technology for stabilization has also increased. Video stabilization algorithms primarily aim at generating stabilized image sequences by removing unwanted shake due to small camera movements. It is important to perform video stabilization in order to assure more effective high level video analysis. In this paper, we propose novel motion correction schemes based on probabilistic filters in the context of block matching motion estimation for efficient video stabilization. We present a detailed overview of the model and compare our model against other block matching schemes on several real-time and synthetic data sets.

BAYES-BASED OBJECT TRACKING BOOSTED BY PARTICLE SWARM OPTIMIZATION

Yuhua Zheng and Yan Meng

This paper presents a novel Bayes-based object tracking framework boosted by a particle swarm optimization (PSO) algorithm, which is a population based searching algorithm. Basically two searching steps are conducted in this method. First, the object model is projected into a high-dimensional feature space, and a PSO algorithm is applied to search over this high-dimensional space and converge to some global optima, which are well-matched candidates in terms of object features. Second, a Bayes-based filter is used to identify the one with the highest possibility among these candidates under the constraint of object motion estimation. The proposed algorithm considers not only the object features but also the object motion estimation to speed up the searching procedure. Experimental results demonstrate that the proposed method is efficient and robust in object tracking.

BAYESIAN ADAPTIVE SAMPLING FOR BIOMASS ESTIMATION WITH QUANTIFIABLE UNCERTAINTY

Pinky Thakkar, Steven M. Crunk, Marian Hofer, Gabriel Cadden, Shikha Naik and Kim T. Ninh

Traditional methods of data collection are often expensive and time consuming. We propose a novel data collection technique, called Bayesian Adaptive Sampling (BAS), which enables us to capture maximum information from minimal sample size. In this technique, the information available at any given point is used to direct future data collection from locations that are likely to provide the most useful observations in terms of gaining the most accuracy in the estimation of quantities of interest. We apply this approach to the problem of estimating the amount of carbon sequestered by trees. Data may be collected by an autonomous helicopter with onboard instrumentation and computing capability, which after taking measurements, would then analyze the currently available data and determine the next best informative location at which a measurement should be taken. We quantify the errors in estimation, and work towards achieving maximal information from minimal sample sizes. We conclude by presenting experimental results that suggest our approach towards biomass estimation is more accurate and efficient compared to the random sampling.

COMPARISON OF FOCUS MEASURES IN FACE DETECTION ENVIRONMENTS

J. Lorenzo, O. Déniz, M. Castrillón and C. Guerra

This work presents a comparison among different focus measures used in the literature for autofocusing in a non previously explored application of face detection. This application has different characteristics to those where traditionally autofocus methods have been applied like microscopy or depth from focus. The aim of the work is to find if the best focus measures in traditional applications of autofocus have the same performance in face detection applications. To do that six focus measures has been studied in four different settings from the oldest to more recent ones.

REPUTATION BASED BUYER STRATEGY FOR SELLER SELECTION FOR BOTH FREQUENT AND INFREQUENT PURCHASES

Sandhya Beldona and Costas Tsatsoulis

Previous research in the area of buyer strategies for choosing sellers in ecommerce markets has focused on frequent purchases. In this paper we present a reputation based buyer strategy for choosing sellers in a decentralized, open, uncertain, dynamic, and untrusted B2C ecommerce market for frequent and infrequent purchases. The buyer models the reputation of the seller after having purchased goods from it. The buyer has certain expectations of quality and the reputation of a seller reflects the seller’s ability to provide the product at the buyer’s expectation level, and its price compared to its competitors in the market. The reputation of the sellers and the price quoted by the sellers are used to choose a seller to transact with. We compare the performance of our model with other strategies that have been proposed for this kind of market. Our re-sults indicate that a buyer using our model experiences a slight improvement for frequent purchases and significant improvement for infrequent purchases.

ARTIFICIAL IMMUNE FILTER FOR VISUAL TRACKING

Alejandro Carrasco E. and Peter Goldsmith

Visual tracking is an important part of artificial Vision for robotics. It allows robots to move towards a desired position using real world information. In this paper we present a novel particle filtering method for visual tracking, based on a clonal selection and a somatic mutation processes used by the natural immune system, which is excellent at identifying intrusion cells; antigens. This capability is used in this work to track motion of the object in a sequence of images.

ON THE BALANCING CONTROL OF HUMANOID ROBOT

Youngjin Choi and Doik Kim

This paper proposes the kinematic resolution method of CoM(center of mass) Jacobian with embedded motions, and the design method of posture/walking controller for humanoid robots. First, the kinematic resolution of CoM Jacobian with embedded motions makes a humanoid robot balanced automatically during movement of all other limbs. Actually, it offers an ability of WBC(whole body coordination) to humanoid robot. Second, the posture/walking controller is completed by adding the CoM controller minus the ZMP(zero moment point) controller to the suggested kinematic resolution method. Also, we prove that the proposed posture/walking controller brings the ISS(disturbance input-to-state stability) for the simplified bipedal walking robot model. Finally, the effectiveness of the suggested posture/walking control method is shown through experiment in regard to the arm dancing of humanoid robot.

A MODIFIED IMPULSE CONTROLLER FOR IMPROVED ACCURACY OF ROBOTS WITH FRICTION

Stephen van Duin, Christopher D. Cook, Zheng Li and Gursel Alici

This paper presents a modified impulse controller to improve the steady state positioning of a SCARA robot having characteristics of high non-linear friction. A hybrid control scheme consisting of a conventional PID part and an impulsive part is used as a basis to the modified controller. The impulsive part uses short width torque pulses to provide small impacts of force to overcome static fiction and move a robot manipulator towards its reference position. It has been shown that this controller can greatly improve a robot’s accuracy. However, the system in attempting to reach steady state will inevitably enter into a small limit cycle whose amplitude of oscillation is related to the smallest usable impulse. It is shown in this paper that by modifying the impulse controller to adjust the width of successive pulses, the limit cycle can be shifted up or down in position so that the final steady state error can be even further reduced.

COMMUNICATION AT ONTOLOGICAL LEVEL IN COOPERATIVE MOBILE ROBOTS SYSTEM

Lucia Vacariu, Mihai Chintoanu, Gheorghe Lazea and Octavian Cret

The cooperation problems in the mobile robots systems can be managed by using a control system based on the software paradigm of multiagent systems. Typical tasks performed by mobile robots systems often assume, among others, dynamic structures and various types of interactions between agents. These are leading to large amounts of information transfer in the communication process and the content and significance of communication is changing continuously. Using an ontological level in the communicating multiagents system can assure the efficiency and the correctness of information exchange between multiagents. The quick change of the content of communication is thus possible and this increases the adaptability of the system. A cooperative mobile robots system for monitoring, manipulating and cleaning in a supermarket is presented. Simulation results with communication at ontological level validate the system cooperation tasks.

ACTIVE 3D RECOGNITION SYSTEM BASED ON FOURIER DESCRIPTORS

E. González, V. Feliú, A. Adán and Luis Sánchez

This paper presents a new 3D object recognition/pose strategy based on reduced sets of Fourier descriptors on silhouettes. The method consists of two parts. First, an off-line process calculates and stores a clustered Fourier descriptors database corresponding to the silhouettes of the synthetic model of the object viewed from multiple viewpoints. Next, an on-line process solves the recognition/pose problem for an object that is sensed by a real camera placed at the end of a robotic arm. The method avoids ambiguity problems (object symmetries or similar projections belonging to different objects) and erroneous results by taking additional views which are selected through an original next best view (NBV) algorithm. The method provides, in very reduced computation time, the object identification and pose of the object. A validation test of this method has been carried out in our lab yielding excellent results.

A STUDY OF TWO COLOR SYSTEMS USED IN CONTENT-BASED IMAGE QUERY ON MEDICAL IMAGERY

Liana Stanescu, Dumitru Burdescu, Cosmin Stoica and Marius Brezovan

The article presents a comparative study over two methods used in content-based visual query. The two methods refer at two different color systems used for representing color information from images: HSV quantized to 166 colors and l1l2l3 quantized to 64 colors. The originality of the study comes from the fact that it was made on a database with medical images from digestive tract area, captured by an endoscope. The scope was to check the quality of the content-based visual query on images representing five different diagnoses (colitis, ulcer, esophagitis, polyps and ulcerous tumor) and taking into consideration that some parameters were modified during the capturing process: viewing direction, intensity and direction of the illumination, parameters that affect mostly the medical images captured during the diagnosis process.

INTERNET-BASED TELEOPERATION: A CASE STUDY - Toward Delay Approximation and Speed Limit Module

Shengtong Zhong, Philippe Le Parc and Jean Vareille

This paper presents the internet-based remote control of mobile robot. To face unpredictable Internet delays and possible connection rupture, a direct teleoperation architecture with “Speed Limit Module” (SLM) and “Delay Approximator” (DA) is proposed. This direct control architecture guarantees the path error of the robot motion is restricted within the path error tolerance of the application. Experiment results show the effectiveness and applicability of this direct internet control architecture in the real internet environment.

BEHAVIOR ANALYSIS OF PASSENGER’S POSTURE AND EVALUATION OF COMFORT CONCERNING OMNI-DIRECTIONAL DRIVING OF WHEELCHAIR

Yuta Sato, Yoshiyuki Noda, Takanori Miyoshi and Kazuhiko Terashima

The purpose of this study is to analyze the relationship between passenger's posture behavior and comfort while riding omni-directional wheelchair. First, an algorithm to transform the obtained data in the sensor coordinates using acceleration sensor into the vehicle coordinates by means of proposed correction algorithm. Its effectiveness is demonstrated by experiments. Second, analysis on the relationship between acceleration of wheelchair movement, passenger's posture behavior and comfort sensation in the riding motion to forward, backward and lateral direction is studied. Posture behavior of passenger's head and chest is measured by acceleration sensors, and comfort sensation of passenger is evaluated by applying the Semantic Differential (SD) method and a Paired Comparison Test. Finally, through a lot of experiment, influence factors concerning comfort while riding to wheelchair are discussed.

X3DIRECTOR - A Front-end For Modeling Web Virtual World Navigation And Visualization Parameters

Berta Buttarazzi and Daniele Pizziconi

Currently, the last step in the evolution of digital technologies for web interface and contents is represented by virtual environments which, despite the appearances, are, in some cases, very accessible and economic. In fact, more than script and vectorial graphic languages, virtual technologies can be used by inexpert users. This paper will provide a panorama on the functionalities of language X3D and the identification of the cases in which is possible to use X3Director, a Front-end For Modeling Web Virtual World Navigation And Visualization Parameters

MODELING ON MOLTEN METAL’S PRESSURE IN AN INNOVATIVE PRESS CASTING PROCESS USING GREENSAND MOLDING AND SWITCHING CONTROL OF PRESS VELOCITY

Ryosuke Tasaki, Yoshiyuki Noda, Kazuhiko Terashima, Kunihiro Hashimoto and Yuji Suzuki

This paper presents modeling and control of fluid pressure inside a mold in a press casting process using greensand molding as an innovative casting method. The defect-free manufactures of casting product in the press process are very important problem. Then, it is made clear that the press velocity control achieves to reduce the rapid increase of fluid pressure. A mathematical model of the molten metal's pressure in a casting mold is built by using a simplified mold and investigated the availability by comparison with the CFD model. A pattern of the press velocity from the high speed to the lower speed is derived by using the mathematical model. Finally, the effectiveness of the proposed switching velocity control has been demonstrated through CFD computer simulations.

PREDICTIVE CONTROL BY LOCAL VISUAL DATA - Mobile Robot Model Predictive Control Strategies Using Local Visual Information and Odometer Data

Lluis Pacheco and Ningsu Luo

Nowadays, the local visual perception research, applied to autonomous mobile robots, has succeeded in some important objectives, such as feasible obstacle detection and structure knowledge. This work relates the on-robot visual perception and sensor fusion information with the nonlinear mobile robot control system, consisting in a differential driven robot with a free rotating wheel. The description of the proposed algorithms can be considered as an interesting aspect of this report. It is developed an easily portable methodology to plan the goal achievement by using the visual data as an available source of positions. Moreover, the dynamic interactions of the robotic system arise from the knowledge of a set of experimental robot models that allow the development of model predictive control strategies based on the mobile robot platform PRIM available in the Laboratory of Robotics and Computer Vision. The meaningful contribution is the use of the local visual information in order to reach a local optimal trajectory planning that approaches the robot to the final desired configuration, while avoiding obstacle collisions. Hence, the research is focused on the experimental aspects. Finally, conclusions on the overall work are drawn.

NEW APPROACH TO GET AUTONOMOUS AND FAST ROBOT LEARNING PROCESSES

R. Iglesias, M. Rodríguez, C. V. Regueiro, J. Correa, Pablo Quintía and S. Barro

Research on robot techniques that are fast, user-friendly, and require little application-specific knowledge by the user, is more and more encouraged in a society where the demand of home-care or domestic-service robots is increasing continuously. In this context we propose a methodology which is able to achieve fast convergences towards good robot-control policies, and reduce the random explorations the robot needs to carry out in order to find the solutions. The performance of our approach is due to the mutual influence that three different elements exert on each other:reinforcement learning, genetic algorithms, and a dynamic representation of the environment around the robot. In fact, the influence of each element over the rest varies along the learning process. We have applied our proposal to teach a robot how to solve two different tasks usual in many robot applications: wall following and door traversal. Through our proposal the robot was able to learn very good control policies for both tasks in a short period of time even though the representation of the environment around the robot was designed for the wall following behaviour and not for the door traversal.

FURTHER STUDIES ON VISUAL PERCEPTION FOR PERCEPTUAL ROBOTICS

Ozer Ciftcioglu, Michael S. Bittermann and I. Sevil Sariyildiz

Further study on computer-based perception by vision modelling are described. The visual perception is mathematically modelled, where the model receives and interprets visual data from the environment. The perception is defined in probabilistic terms so that it is in the same way quantified. At the same time, the measurement of visual perception is made possible in real-time. Quantifying visual perception is essential for information gain calculation. Providing virtual environment with appropriate perception distribution is important for enhanced distance estimation in virtual reality. Computer experiments are carried out by means of a virtual agent in a virtual environment demonstrating the verification of the theoretical considerations being presented, and the far reaching implications of the studies are pointed out.

METHODOLOGY FOR LEARNING VISUAL REACTIVE BEHAVIOURS IN ROBOTICS THROUGH REINFORCEMENT AND IMAGE-BASED STATES

Pablo Quintía, José E. Domenech, Cristina Gamallo and Carlos V. Regueiro

In this article the development of a methodology for the learning of visual and reactive behaviours using reinforcement learning is described. With the use of artificial vision the environment is perceived in 3D, and it is possible to avoid obstacles that are invisible to other sensors that are more common in mobile robotics. Reinforcement learning reduces the need for intervention in behaviour design, and simplifies its adjustment to the environment, the robot and the task. The designed methodology aims to be general, so in order to change the desired behaviour only the reinforcement and the filtering of the image need to be changed. For the definition of the reinforcement a laser sensor is used and for the definition of the states a fixed 3x3 grid is used. The behaviours learned were wall following, object following, corridor following and platform following. Results are presented with a Pioneer 2 AT. Learning and testing phase has been realized on the Gazebo 3D simulator, and a test of the wall following behaviour was carried out on a real environment.

AN IMPLEMENTATION OF HIGH AVAILABILITY IN NETWORKED ROBOTIC SYSTEMS

Florin Daniel Anton, Theodor Borangiu and Silvia Anton

In today’s complex enterprise environments, providing continuous service for applications is a key component of a successful robotized implementing of manufacturing. High availability (HA) is one of the components contributing to continuous service provision for applications, by masking or eliminating both planned and unplanned systems and application downtime. This is achieved through the elimination of hardware and software single points of failure (SPOF). A high availability solution will ensure that the failure of any component of the solution - either hardware, software or system management, will not cause the application and its data to become permanently unavailable. High availability solutions should eliminate single points of failure through appropriate design, planning, hardware selection, software configuring, application control, carefully environment control and change management discipline. In short, one can define high availability as the process of ensuring an application is available for use by duplicating and/or sharing hardware resources managed by a specialized software component. A high availability solution in robotized manufacturing provides automated failure detection, diagnosis, application recovery, and node (robot controller) re integration. The paper discusses the implementing of a high availability solution in a robotized manufacturing line.

BELL SHAPED IMPEDANCE CONTROL TO MINIMIZE JERK WHILE CAPTURING DELICATE MOVING OBJECTS

Arjun Nagendran, Robert C Richardson and William J. Crowther

Catching requires the ability to predict the position and intercept a moving object at relatively high speeds. Because catching is a contact task, it requires an understanding of the interaction between the forces applied and position of the object being captured. The application of force to a mass results in a change in acceleration. The rate of change of acceleration is called jerk. Jerk causes wear on the manipulator over time and can also damage the object being captured. This paper uses a curve that asymptotes to zero gradient at +/- infinity to develop an impedance controller, to decelerate an object to a halt after it has been coupled with the end effector. It is found that this impedance control method minimizes the jerk that occurs during capture, and eliminates the jerk spikes that are existent when using spring dampers, springs or constant force to decelerate an object.

TELEOPERATION OF COLLABORATIVE MOBILE ROBOTS WITH FORCE FEEDBACK OVER INTERNET

Ivan Petrovic, Josip Babic and Marko Budisic

A teleoperation system has been developed that enables two human operators to safely control two collaborative mobile robots in unknown and dynamic environments from any two PCs connected to the Internet by installing developed client program on them and by using simple force feedback joysticks. On the graphical user interfaces, the operators receive images forwarded by the cameras mounted on the robots, and on the the joysticks they feel forces forwarded by developed obstacle prevention algorithm based on the dynamic window approach. The amount and direction of the forces they feel on their hands depend on the distance and direction to the robot's closest obstacle,which can also be the collaborating robot. To overcome the instability caused by the unknown and varying time delay an event-based teleoperation method is employed to synchronize actions of each robot with commands from its operator. Through experimental investigation it is confirmed that developed teleoperation system enables the operators to successfully accomplish collaborative tasks in complex environments.

COORDINATED MOTION CONTROL OF MULTIPLE ROBOTS

Wojciech Kowalczyk and Krzysztof Kozlowski

In this paper a set of robot coordination approaches is presented. Described methods are based on formation function concept. Accuracy of different approaches is compared using formation function time graphs. Virtual structure method is analyzed, then virtual structure expanded with behavioral formation feedback is presented. Finally leader-follower scheme is described. Presented methods are illustrated by simulation results. Differentially driven nonholonomic mobile robots were used in simulations.

ENTANGLEMENT DETECTION OF A SWARM OF TETHERED ROBOTS IN SEARCH AND RESCUE APPLICATIONS

T. R. Vishnu Arun Kumar and R. C. Richardson

In urban search and rescue (USAR) applications, robots play a pivotal role. As USAR is time sensitive, swarm of robots is preferred over single robot for victim search. Tethered robots are widely used in USAR applications because tether provides a means of robust data communication and power supply. The problem with using tethers in a collapsed, unstructured environment is tether entanglement. Entanglement detection becomes vital in this scenario. This paper presents a novel, low-cost approach to detect entanglement in the tether connecting two mobile robots. The proposed approach requires neither the robots to be localized nor an environment map. Experimental results show that the proposed approach is effective in identifying tether entanglement.

VISION-BASED OBSTACLE AVOIDANCE FOR A SMALL, LOW-COST ROBOT

Chau Nguyen Viet and Ian Marshall

his paper presents a vision-based obstacle avoidance algorithm for a small indoor mobile robot built from low-cost, and off-the-shelf electronics. The obstacle avoidance problem in robotics has been researched extensively and there are many well established algorithms for this problem.However, most of these algorithms are developed for large robots with expensive, specialised sensors, and powerful computing platforms. We have developed an algorithm that can be implemented on very small robots with low-cost electronics and small computing platforms. Our vision-based obstacle detection algorithm is fast and works with very low resolution images. The control mechanism utilises both visual information and sonar sensor's measurement without having to fuse the data into a model or common representation. T he robot platform was tested in an unstructured office environment and demonstrated a reliable obstacle avoidance behaviour.

THE PROTOTYPE OF HUMAN – ROBOT INTERACTIVE VOICE CONTROL SYSTEM

Miroslav Holada, Igor Kopetschke, Pavel Pirkl, Martin Pelc, Lukáš Matela, Jiří Horčička and Jakub Štilec

This contribution shows our recent activities of human – robot control system. The keynote consists in design system that allows easy control and programming of various robots by uniform interactive system. Generally the robots are actuate by sets of control commands, sometimes by hand control interface (touchpad, joystick). The operator had to know control commands, syntax rules and other properties necessary for successful robot control. Our system offers uniform commands like “move left” or “elevate arm” that are translated and sent into corresponding device (robot). Speech recognition is based on isolated-word HMM engine with distributed recognition system (DSR). The vocabulary can contain thousands word and short phrases. It allows us design easy dialogue scenario for robot control system. The system allows direct robot control like moving or programming short sequences. The video camera is included for displaying working place. The algorithms of image processing allow to detect the position of objects in working area and facilitate robot navigation.

ROBOT TCP POSITIONING WITH VISION - Accuracy Estimation of a Robot Visual Control System

Drago Torkar and Gregor Papa

A calibrated 3D visual servoing has not fully matured as a technology yet. In order to widen its use in industrial applications its technological capability must be precisely known. The accuracy and repeatability are two of the crucial parameters in planning of any robotic task. In this paper we describe a procedure to evaluate the 2D the 3D accuracy of a robot stereo vision system consisted of two identical 1 Megapixel cameras, and present the results of the evaluation.

HYBRID MOTION CUEING ALGORITHMS FOR REDUNDANT ADVANCED DRIVING SIMULATORS

Hatem Elloumi, Nadia Maïzi and Marc Bordier

Redundant Advanced Driving Simulators (hexapods mounted on rails) present an extra capability to reproduce motion sensations. The exploitation of this capability is currently done by frequency separation methods without taking into account the frequency overlapping between the hexapod and the rails. Within this bandwidth, these two degrees of freedom could be considered as equivalent. Our aim is to use this equivalence to improve the motion restitution. We offer two algorithms based on the hybrid systems framework which deal with the longitudinal mode. Their goal is to improve the restitution of motion sensations by reducing false cues (generated by actuators braking) and decreasing null cues (due to actuators blocking). Our algorithms include and treat all steps of motion cueing: motion tracking (restitution), braking before reaching the displacement limits, washout motion, and switching rules.

ESCAPE LANES NAVIGATOR - To Control “RAOUL” Autonomous Mobile Robot

Nicolas Morette, Cyril Novales and Laurence Josserand

This paper presents a navigation method to control autonomous mobile robots : the escapes lanes, applied to RAOUL mobile system. First, the formalism is introduced to model an automated system, and then is applied to a mobile robot. Then, this formalism is used to describe the escape lanes navigation method, and is applied to our RAOUL mobile robot. Finally, implementation results simulations validate the concept.

HUMAN-SCALE VIRTUAL REALITY CATCHING ROBOT SIMULATION

Ludovic Hamon, François-Xavier Inglese and Paul Richard

This paper presents a human-scale virtual reality catching robot simulation. The virtual robot catches a ball that users throw in its workspace. User interacts with the virtual robot using a large-scale bimanual haptic interface. This interface is used to track user’s hands movements and to display weight and inertia of the virtual balls. Stereoscopic viewing, haptic and auditory feedbacks are provided to improve user’s immersion and simulation realisms.

REAL-TIME INTER- AND INTRA- CAMERA COLOR MODELING AND CALIBRATION FOR RESOURCE CONSTRAINED ROBOTIC PLATFORMS

Walter Nisticò and Matthias Hebbel

This paper presents an approach to correct chromatic distortion within an image (vignetting) and to compensate for color response differences among similar cameras which equip a team of robots, based on Evolutionary Algorithms. Our black-box approach does not make assumptions concerning the physical/geometrical roots of the distortion, and the efficient implementation is suitable for real time applications on resource constrained platforms.

SCADA WEB - Remote Supervision and Maintenance of Industrial Processes

José Ramón Janeiro, Eduardo J. Moya, David García, Oscar Calvo and Clemente Cárdenas

This article explains a SCADA System called SCADA Web developed by CARTIF Foundation. This SCADA System makes possible remote monitoring and control from the process controlled by PLC using Web Technology. SCADA Web has been developed on platform Java EE and provides visualization and control signals, trend charts, alarms, historical data and alarm reports.

A NOVEL STRATEGY FOR EXPLORATION WITH MULTIPLE ROBOTS

Jonathan Rogge and Dirk Aeyels

The present paper develops a novel strategy for the exploration of an unknown environment with a multi-robot system. Contrary to most exploration problems, the topographical properties of the space need not be mapped. The algorithm we propose is inspired by methods used for complete coverage of an area, where all free space has to be physically covered by all robots. In the present paper it is required that the entire free space is covered by the {\it sensors} of the robots, with a certainty of $100\%$. This weaker requirement enables us to scan more space in less time, compared to complete coverage algorithms. Moreover the shape of the robot formation adjusts itself to situations where obstacles, narrow spaces, etc. have to be passed. Communication between the robots is restricted to line-of-sight and to a maximum interdistance between robots. A direct application of the algorithm is mine field clearance.

IMPROVED OCCUPANCY GRID LEARNING - The ConForM Approach to Occupancy Grid Mapping

Thomas Collins, J. J. Collins and Conor Ryan

A central requirement for the development of robotic systems, that are capable of autonomous operation in non-specific environments, is the ability to create maps of their operating locale. These maps can subsequently serve as the basis for planning and problem solving activities. The creation of these maps is a non trivial process as the robot has to interpret the findings of its sensors so as to make deductions regarding the state of its environment. Current approaches to the robotic mapping problem fall into two broad categories: on-line and offline. An on-line approach is characterised by its ability to construct a map as the robot traverses its operating environment, however this comes at the cost of representational clarity. An offline approach on the other hand requires that all sensory data be gathered before processing begins but is capable of creating more accurate maps. In this paper we present a new means of constructing occupancy grid maps which addresses this problem.

THE TELE-ECHOGRAPHY MEDICAL ROBOT OTELO2 - Teleoperated with a Multi Level Architecture using Trinomial Protocol

Gwenaël Charron, Aïcha Fonte, Pierre Vieyres Philippe Fraisse, Lama Al Bassit and Cyril Novales

This paper presents a novel architecture applied to a mobile teleoperated medical robotic system: OTELO2 (MObile Tele-Echography using an Ultra-Light RObot); OTELO2 performs a tele-echography at a distance for the benefit of medically isolated sites. First, this paper presents an overview of the OTELO2 teleoperated system. Then, it describes the modular control architecture used and the integration of the teleoperated layer on this multi level architecture. Finally, it presents the communication links used to control this system, as well as some experimental results.

MATHEMATICAL MODEL FOR WALKING ROBOT WITH SHAPE MEMORY ALLOY ANKLE

Anca Petrişor, Nicu George Bîzdoacă, Daniela Roşca, Sonia Degeratu, Adrian Roşca and Raducu Petrisor

The paper present a simultaneous force and length variation mode shape memory alloy (SMA) robotic application. The robotic ankle contains four SMA actuators and a spherical articulation. In order to assure a high efficient robotic architecture, the mechanical and the control structure have to assure a real-time response to the work environment changes. The load variations or the difference between the moment of full contact step and the non contact moment for a waking robot are the standard situations for a SMA robotic ankle. The paper is divided in five sections. First section makes a short introduction in the physical description and conventional applications of shape memory alloy materials. Then are presented the mathematical model for robotic ankle, the walking robot geometrical structure and the causality ordering of the active pair of legs, in this case with one free joint. In the last section are presented some experimental results obtained by using a platform, conceived by authors in MATLAB environment, for design and simulation of walking robots control algorithms.

KAMANBARÉ - A Tree-climbing Biomimetic Robotic Platform for Environmental Research

Reinaldo de Bernardi and José Jaime da Cruz

Environmental research is an area where robotics platforms can be applied as solutions for different problems to help or automate certain tasks, with the purpose of being more efficient or also safer for the researchers involved. This paper presents the Kamanbaré platform. Kamanbaré is a bioinspired robotic platform, whose main goal is to climb trees for environmental research applications, applied in tasks such as gathering botanical specimen, insects, climatic and arboreal fauna studies, among others. Kamanbaré is a platform that provides flexibility both in hardware and software, so that new applications may be developed and integrated without the need of extensive knowledge in robotics.

A MULTIROBOT SYSTEM FOR DISTRIBUTED SENSING

Janne Haverinen, Anssi Kemppainen, Janne Kivijakola and Juha Röning

This paper presents a modular multirobot system developed for distributed sensing experiments. The multirobot system is composed of modular small size robots, which have various sensor capabilities including a color stereo camera system and an infrared based sensor for the relative pose estimation. The paper describes the current state of the multirobot system introducing the robots, their sensor capabilites, and some initial experiments conducted with the system. The experiments include a distributed laser based 3-D scanning experiment involving two robots, and an experiment where a group of robots arrange into a spatial formation and measures the distributions of light and magnetic field of the environment. The experiments demonstrate how a multirobot system can be used in a meaningful way to extract information from the environment, and how they can cooperatively perform tasks, like 3-D scanning, which is a difficult task for a single small size robot due to limitations of current sensing technologies. The 3-D scanning experiment also demonstrates how the multirobot system's inherent properties, i.e. spatial distribution and mobility, can be utilized in a novel way to create distributed measurement devices in which each robot has an role as a part of the device, and in which the mobility of the robots provides flexibility to the structure of the measurement system.

USING SIMPLE NUMERICAL SCHEMES TO COMPUTE VISUAL FEATURES WHENEVER UNAVAILABLE - Application to a Vision-Based Task in a Cluttered Environment

FOLIO David and CADENAT Viviane

In this paper, we propose to apply classical numerical integration algorithms to determine the visual features when they are lost or unavailable during a vision-based navigation task. The developed methods have been tested both in simulation and experimentation, with interesting results. A comparative analysis is also provided to select the most interesting techniques depending on the context of use.

MINIMUM COST PATH SEARCH IN AN UNCERTAIN-CONFIGURATION SPACE

Eric Pierre and Romain Pepy

The object of this paper is to propose a minimum cost path search algorithm in an uncertain-configuration space and to give its proofs of optimality and completeness. In order to achieve this goal, we focus on one of the simpler and efficient path search algorithm in the configuration space : the A* algorithm. We then add uncertainties and deal with them as if they were simple dof (degree of freedom). Next, we introduce towers of uncertainties in order to improve the efficiency of the search. Finally we prove the optimality and completeness of the resulting algorithm.

INCLUSION OF ELLIPSOIDS

Romain Pepy and Eric Pierre

We present, in this paper, a ready-to-use inclusion detection for ellipsoids. Ellipsoids are used to represent configuration uncertainty of a mobile robile. This kind of test is used in path planning to find the optimal path according to a safety criteria.

INTROSPECTION ON CONTROL-GROUNDED CAPABILITIES - A Task Allocation Study Case in Robot Soccer

Christian G. Quintero M., Salvador Ibarra M., Josep Ll. De la Rosa and Josep Vehí

Our proposal is aimed at achieving reliable task allocation in cooperative agent-controlled systems by means of introspection on control-oriented features. This new approach is beneficial as it improves the way agents can coordinate with each other to perform the proposed tasks in a real cooperative environment. Introspection aims at including reliable control-oriented knowledge of the controlled systems in the agents’ decision-making. To that end, control-grounded capabilities, inspired by the agent metaphor, are used in the task utility/cost functions. Such control-grounded capabilities guarantee an appropriate agent-oriented representation of the specifications and other relevant details encapsulated in every automatic controller of the controlled systems. In particular, this proposal is demonstrated in the successful performing of rescue operations by cooperative mobile robots in a simulated environment. Experimental results and conclusions are shown, stressing the relevance of this new approach in the sure and trustworthy attainment of allocated tasks for improving multi-agent performance.

THEORY OF STRUCTURED INTELLIGENCE - Results on Innovation-based and Experience-based Behaviour

Meike Jipp and Essameddin Badreddin

An agreed-upon general theory of intelligence would enable significant scientific progress in all disciplines doing research on intelligence. Such a theory, namely the theory of structured intelligence is tested by relating it to other theories in the field and by empirically testing it. The results demonstrate (1) that the theory of structured intelligence, uses a similar concept of intelligence as do other theories and (2) that its distinction between innovation- and experience-based solutions can be found in the behaviour of the study’s participants. This yields the opportunity to (1) allow technically testing intelligence in an easier and less time-consuming ways as do traditional intelligence tests, and (2) allow technology classifying the intelligence of its user and using adaptive interfaces reducing the possibility of serious handling errors.

A DISTRIBUTED MULTI-ROBOT SENSING SYSTEM USING AN INFRARED LOCATION SYSTEM

Anssi Kemppainen, Janne Haverinen and Juha Röning

Distributed sensing refers to measuring systems where instead of one sensor multiple sensors are spatially distributed improving robustness of the system, increasing relevancy of the measurements and cutting costs since requiring smaller and less precise sensors. Spatially distributed sensors fuse their measurements into the same coordinates requiring relative positions of the sensors. In this paper, we present a distributed multi-robot sensing system in which relative poses (positions and orientations) among robots are estimated using an infrared location system. The relative positions are estimated using intensity and bearing measurements of the received infrared signals. The relative orientations are obtained by fusing position estimates among robots. The location system enables a group of robots to perform distributed and cooperative environment sensing by maintaining a given formation while the group measures distributions of light and magnetic field, for example. In the experiments, a group of three robots moves and collects spatial information (i.e. illuminance and compass heading) from the given environment. The information is stored into grid maps and illustrated in the figures presenting illuminance and compass heading. The experiments proved the feasibility of the distributed multi-robot sensing system for sensing applications where the environment requires moving platforms.

DISTURBANCE FEED FORWARD CONTROL OF A HANDHELD PARALLEL ROBOT

Achim Wagner, Matthias Nübel, Essam Badreddin, Peter P. Pott and Markus L. Schwarz

A model-based control approach for a surgical parallel robot is presented, which combines a local tool stabilization with a global disturbance feed forward control. The robot is held in the operator's hand during the manipulation of bones. For a precise processing the tool has to be decoupled from disturbances due to unintentional hand movements of the surgeon at the robot base. The base disturbances are transformed for a feed forward control using the inverse dynamics of the robot. Simulations show that disturbances can be reduced by many orders depending on sensor errors and delay.

BEHAVIOR BASED DESCRIPTION OF DEPENDABILITY - Defining a Minium Set of Attributes for a Behavioral Description of Dependability

Jan Rüdiger, Achim Wagner and Essam Badreddin

Dependability is widely understood as an integrated concept that consists of different attributes. The set of attributes and requirements of each attribute varies from application to application thus making it very challenging to define dependability for a broad amount of application. The dependability, however, is of great importance when dealing with autonomous or semi-autonomous systems, thus defining dependability for those kind of system is vital. Such autonomous mobile system are usually described by their behavior. In this paper a minimum set of attributes for the dependability of autonomous mobile systems is proposed based on a behavioral definition of dependability.

KINEMATICS AND DYNAMICS ANALYSIS FOR A HOLONOMIC WHEELED MOBILE ROBOT

Ahmed El-Shenawy, Achim Wagner and Essam Badreddin

This paper presents the kinematics and the dynamics analysis of the holonomic mobile robot C3P. The robot has three caster wheels with modular wheel angular velocities actuation. The forward dynamics model which is used during the simulation process is discussed along with the robot inverse dynamics solution. The inverse dynamics solution is used to overcome the singularity problem, which is observed from the kinematic modeling. Since both models are different in principle they are analyzed using simulation examples to show the effect of the actuated and non actuated wheel velocities on the robot response. An experiment is used to illustrate the performance of the inverse dynamic solution practically.

LOW COST SENSING FOR AUTONOMOUS CAR DRIVING IN HIGHWAYS

André Gonçalves, André Godinho and João Sequeira

This paper presents a viability study on autonomous car driving in highways using low cost sensors for environment perception. The solution is based on a simple behaviour-based architecture implementing the standard perception-to-action scheme. The perception of the surrounding environment is obtained through the construction of an occupancy grid based on the processing of data from a single video camera and a small number of ultrasound sensors. A finite automaton integrates a set of primitive behaviours defined after the typical human highway driving behaviors. The system was successfully tested in both simulations and in a laboratory environment using a mobile robot to emulate the car-like vehicle. The robot was able to navigate in an autonomous and safe manner, performing trajectories similar to the ones carried out by human drivers. The paper includes results on the perception obtained in a real highway that support the claim that low cost sensing can be effective in this problem.

COLLABORATIVE CONTROL IN A HUMANOID DYNAMIC TASK

Diego Pardo and Cecilio Angulo

This paper describes a control scheme that allows to govern the dynamic behavior of an articulated mobile robot with several degrees of freedom (DOF) and redundancies. These types of robots need a high level of coordination between the motors performance to complete their motions. A collaborative scheme is employed, where the actuators involved in a specific task share information, computing integrated control actions. The control functions are found using a stochastic reinforcement learning technique allowing the robot to automatically generate them based on experiences. This type of control is based on a modularization principle: complex overall behavior is the result of the interaction of individual simple components.\\ Unlike the standard procedures, the control approach is not meant to follow a trajectory generated by a planner, instead, it emerges as a consequence of the collaboration between joints movements that seek to achieve a goal. The learning of the sensorimotor coordination in a simulated humanoid is presented as a demonstration.

GRASP CONFIGURATION MATCHING - Using Visual and Tactile Sensor Information

Madjid Boudaba and Alicia Casals

Finding the global shape of a grasped object directly from touch is time consuming and not highly reliable. This paper describes the relationship between visual features and grasp planning, and correlates visual and tactile information for a better description of the object's shape and grasping points determination. The grasping process proposed is experimented with a three fingered robotic hand.

A HUMAN AIDED LEARNING PROCESS FOR AN ARTIFICIAL IMMUNE SYSTEM BASED ROBOT CONTROL - An Implementation on an Autonomous Mobile Robot

Jan Illerhues and Nils Goerke

In this paper we introduce a pre-structured learning process that enables a teacher to build a robot control. This controller maps sensor data of a real, autonomous robot to its motor values. The kind of mapping is defined by a teacher. The learning process is divided into four phases and leads the agent from a supervised learning system to a reinforcement learning, and finally to an autonomous learning system. At the beginning the controller starts completely without knowledge, and learns some new behaviours in the first two phases by interacting with its teacher. In the third phase of the learning process the teacher evaluates the states which the robot achieves by performing the behaviours taught in phase 1 and 2. In the fourth phase the robot gains experience by evaluating the transitions of the different behavioral states. The result of each process phase is stored in a rule-like association system (RLA), which is derived from the artificial immune system. The experience gained throughout the whole learning process serves as a knowledge base for planning actions to complete a task given by the teacher. This paper presents the learning process, its implementation, and its results.

AN INCREMENTAL MAPPING METHOD BASED ON A DEMPSTER-SHAFER FUSION ARCHITECTURE

Melanie Delafosse, Laurent Delahoche, Arnaud Clerentin and Anne-Marie Jolly-Desodt

Firstly this article presents a multi-level architecture permitting the localization of a mobile platform and secondly an incremental construction of the environment’s map. The environment will be modeled by an occupancy grid built with information provided by the stereovision system situated on the platform. The reliability of these data is introduced to the grid by the propagation of uncertainties managed thanks to the theory of the Transferable Belief Model.

OPTICAL NAVIGATION SENSOR - Incorporating Vehicle Dynamics Information in Mapmaking

Tibor Takács and Viktor Kálmán

Accurate odometry and navigation may well be the most important tasks of a mobile robot’s control system. To solve this task it is necessary to utilize proper sensors which provide reliable information about the motion. This paper presents the prototype of an optical correlation sensor which can be an alternative choice for the dead reckoning navigation system of a vehicle or mobile robot. The last part of the paper presents an other application in an inertial navigation system that enables a new approach to map making which incorporates vehicle dynamics into the world map.

FORMAL VERIFICATION OF SAFETY BEHAVIOURS OF THE OUTDOOR ROBOT RAVON

Martin Proetzsch, Karsten Berns, T. Schuele and K. Schneider

This paper presents an approach to the formal verification of safety properties of the behaviour-based control network of the mobile outdoor robot RAVON. In particular, we consider behaviours that are used for the computation of the projected vehicle's velocity from obstacle proximity sensor data and inclination information. We describe how this group of behaviours is implemented in the synchronous language Quartz in order to be formally verified using model checking techniques of the Averest verification framework. Moreover, by integrating the automatically generated and verified code into the behaviour network, it can be guaranteed that the robot slows down and stops as required by the given safety specifications.

OBSTACLE DETECTION IN MOBILE OUTDOOR ROBOTS - A Short-term Memory for the Mobile Outdoor Platform RAVON

H. Schäfer, M. Proetzsch and K. Berns

In this paper a biologically inspired approach for compensating the limited angle of vision in obstacle detection systems of mobile robots is presented. Most of the time it is not feasible to exhaustively monitor the environment of a mobile robot. In order to nonetheless achieve safe navigation obstacle detection mechanisms need to keep in mind certain aspects of the environment. In mammals this task is carried out by the creature's short-term memory. Inspired by this concept an absolute local map storing obstacles in terms of representatives has been introduced in the obstacle detection and avoidance system of the outdoor robot RAVON. That way the gap between the fields of vision of two laser range finders can be monitored which prevents the vehicle from colliding with obstacles seen some time ago.

A COMPUTATIONALLY EFFICIENT GUIDANCE SYSTEM FOR A SMALL UAV

Guillaume Ducard and Hans P. Geering

In this paper, a computationally efficient guidance algorithm has been designed for a small aerial vehicle. It is capable of avoiding known no-fly zones (NFZ) or obstacles detected during the flight and also capable of taking into account reduction in the aircraft performance. Preflight path planning only consists in storing a few way points guiding the aircraft to its target. The paper presents an efficient way to model no-fly zones and to generate a path in real-time to avoid the obstacle, even in the event of reduced aircraft performance.

DESIGN OF LOW INTERACTION DISTRIBUTED DIAGNOSERS FOR DISCRETE EVENT SYSTEMS

J. Arámburo-Lizárraga, E. López-Mellado and A. Ramírez-Treviño

This paper deals with distributed fault diagnosis of discrete event systems (DES). The approach held is model based: an interpreted Petri net (IPN) describes both the normal and faulty behaviour of DES in which both places and transitions may be non measurable. The diagnoser monitors the evolution of the DES outputs according to a model that describes the normal behaviour of the DES. A method for designing a set of distributed diagnosers is proposed; it is based on the decomposition of the DES model into reduced sub-models which require minimal interaction among them; the diagnosability property is studied for the set of resulting sub-models.

GEOMETRIC CONTROL OF A BINOCULAR HEAD

Eduardo Bayro-Corrochano and Julio Zamora-Esquivel

In this paper the authors use geometric algebra to formulate the differential kinematics of a binocular robotic head and reformulate the interaction matrix in terms of the lines that represent the principal axes of the camera. This matrix relates the velocities of 3D objects and the velocities of their images in the stereo images. Our main objective is the formulation of a kinematic control law in order to close the loop between perception and action, which allows to perform a smooth visual tracking.

GEOMETRIC ADVANCED TECHNIQUES FOR ROBOT GRASPING USING STEREOSCOPIC VISION

Julio Zamora-Esquivel and Eduardo Bayro-Corrochano

In this paper the authors propose geometric techniques to deal with the problem of grasping of the objects relaying on their mathematical models. For that we use the geometric algebra framework to formulate the kinematics of a three finger robotic hand. Our main objective is by formulating a kinematic control law to close the loop between perception and actions. This allows us to perform a smooth visually guided object grasping.

DYNAMIC SENSOR NETWORKS: AN APPROACH TO OPTIMAL DYNAMIC FIELD COVERAGE

Simone Gabriele and Paolo Di Giamberardino

In the paper a solution to the sensor network coverage problem is proposed, based on the usage of moving sensors that allow a larger fiels coverage using a smaller number of devices. The problem than moves from the optimal allocation of fixed or almost fixed sensors to the determination of optimal trajectories for moving sensors. In the paper a suboptimal solution obtained from the sampled optimal problem is given. First, in order to put in evidence the formulation and the solution approach to the optimization problem, a single moving sensor has been addresed. Then, the results for multisensor systems are shown. Some simulation results are also reported to show the behaviour of the sensors network.

HOMOGRAPHY-BASED MOBILE ROBOT MODELING FOR DIGITAL CONTROL IMPLEMENTATION

Andrea Usai and Paolo Di Giamberardino

The paper addresses the development of a kinematic model for a system composed by a nonholonomic mobile robot and a camera used as feedback sensor to close the control loop. It is shown that the proposed homography-based model takes a particular form that brings to a finite sampled equivalent model. The design of a multirate digital controlis then descibed and discussed, showing that exact solutions are obtained. Simulation results are also reported toput in evidence the effectiveness of the solution proposed.

FEATURE DETECTION ALGORITHM FOR AUTONOMOUS CAMERA CALIBRATION

Kyung min Han and Guilherme N. DeSouza

This paper presents an adaptive and robust algorithm for automatic corner detection. Ordinary camera calibration methods require that a set of feature points – usually, corner points of a chessboard type of pattern – be presented to the camera in a controlled manner. On the other hand, the proposed approach automatically locates the feature points even in the presence of cluttered background, change in illumination, arbitrary poses of the pattern, etc. As the results demonstrate, the proposed technique is much more appropriate to automatic camera calibration than other existing methods.

A DUAL MODE ADAPTIVE ROBUST CONTROLLER FOR DIFFERENTIAL DRIVE TWO ACTUATED WHEELED MOBILE ROBOT

Samaherni M. Dias, Aldayr D. Araujo and Pablo J. Alsina

This paper is addressed to dynamic control problem of nonholonomic differential wheeled mobile robot. It presents a dynamic controller to mobile robot, which requires only information of the robot configuration, that are collected by an absolute positioning system. The control strategy developed uses a linear representation of mobile robot dynamic model. This linear model is decoupled into two single-input single-output systems, one to linear displacement and one to angle of robot. For each resulting system is designed a dual-mode adaptive robust controller, which uses as inputs the reference signals calculated by a kinematic controller. Finally, simulation results are included to illustrate the performance of the closed loop system.

ON GENERATING GROUND-TRUTH TIME-LAPSE IMAGE SEQUENCES AND FLOW FIELDS

Vladimír Ulman and Jan Hubený

The availability of time-lapse image sequencies accompanied with appropriate ground-truth flow fields is crucial for quantitative evaluation of any optical flow computation method. Moreover, since these methods are often part of automatic object-tracking or motion-detection solutions used mainly in robotics and computer vision, an artificially generated high-fidelity test data is obviously needed. In this paper, we present a framework that allows for automatic generation of such image sequences based on real-world model image together with an artificial flow field. The framework benefits of a two-layered approach in which user-selected foreground is locally moved and inserted into an artificially generated background. The background is visually similar to the input real image while the foreground is extracted from it and so its fidelity is guaranteed. The framework is capable of generating 2D and 3D image sequences of arbitrary length. A brief discussion as well as an example of application in optical microscopy imaging is presented.

ON THE FORCE/POSTURE CONTROL OF A CONSTRAINED SUBMARINE ROBOT

Ernesto Olguín-Díaz and Vicente Parra-Vega

An advanced, yet simple, controller for submarine robots is proposed to achieve simultaneously tracking of time-varying contact force and posture, without any knowledge of its dynamic parameters. Structural properties of the submarine robot dynamic are used to design passivity-based controllers. A representative simulation study provides additional insight into the closed-loop dynamic properties for regulation and tracking cases. A succinct discussion about inherent properties of the open-loop and closed-loop systems finishes this work.

DEVELOPMENT OF AN AUTOMATED DEVICE FOR SORTING SEEDS - Application on Sunflower Seeds

Vincent Muracciole, Patrick Plainchault, Dominique Bertrand and Maria Rosaria Mannino

Purity analysis and determination of other seeds by number are still made manually. It is a repetitive task based upon visual analysis. Our work objective is to create and use a simple and quick automated system to do this task. A first step of this machine has been reached by validating the image acquisition and feeding process. The principle of this machine is based on a seeds fall with stroboscopic effect image acquisition. This article presents the first step of creating a dedicated and autonomous machine which combines embedded constraints and real time processes.

A NEW PROBABILISTIC PATH PLANNER - For Mobile Robots Comparison with the Basic RRT Planner

Sofiane Ahmed Ali, Eric Vasselin and Alain Faure

The rapidly exploring random trees (RRTs) have generated a highly successful single query planner which solved difficult problems in many applications of motion planning in recent years. Even though RRT works well on many problems, they have weaknesses in environments that handle complicated geometries. Sampling narrow passages in a robot’s free configuration space remains a challenge for RRT planners indeed; the geometry of a narrow passage affects significantly the exploration property of the RRT when the sampling domain is not well adapted for the problem. In this paper we characterize the weaknesses of the RRT planners and propose a general framework to improve their behaviours in difficult environments. We simulate and test our new planner on mobile robots in many difficult static environments which are completely known, simulations show significant improvements over existing RRT based planner to reliably capture the narrow passages areas in the configuration space

AN AUGMENTED STATE VECTOR APPROACH TO GPS-BASED LOCALIZATION

Francesco Capezio, Antonio Sgorbissa and Renato Zaccaria

The ANSER project (Airport Night Surveillance Expert Robot) is described, exploiting a mobile robot for autonomous surveillance in civilian airports and similar wide outdoor areas. The paper focuses on the localization subsystem of the patrolling robot, composed of a non-differential GPS unit and a laser rangefinder for map-based localization (inertial sensors are absent). Moreover, it shows that an augmented state vector approach and an Extended Kalman filter can be successfully employed to estimate the colored components in GPS noise, thus getting closer to the conditions for the EKF to be applicable

FORWARD KINEMATICS AND GEOMETRIC CONTROL OF A MEDICAL ROBOT - Application to Dental Implantation

Richard Chaumont, Eric Vasselin and Dimitri Lefebvre

Recently, robotics has found a new field of research in surgery in which it is used as an assistant of the surgeon in order to promote less traumatic surgery and minimal incision of soft tissue. In accordance with the requirements of dental surgeons, we offer a robotic system dedicated to dental implants. A dental implant is a mechanical device fixed into the patient’s jaw. It is used to replace a single tooth or a set of missing teeth. Fitting the implant is a difficult operation that requires great accuracy. This work concerns the prototype of a medical robot. Forward and inverse kinematics as dynamics are considered in order to drive a control algorithm which is as accurate and safe as possible.

HIGHER ORDER SLIDING MODE STABILIZATION OF A CAR-LIKE MOBILE ROBOT

F. Hamerlain, K. Achour, T. Floquet and W. Perruquetti

In this paper, we are concerned with the robust stabilization of a car-like mobile robot given in a perturbed chained form. A higher order sliding mode control strategy is elaborated. This control strategy switches between two different sliding mode controls: a second order one (super-twisting algorithm) and a new third order sliding mode control that performs a finite time stabilization. The proposed third sliding mode controller is based on geometric homogeneity property with a discontinuous term. Simulation results show the control performance.

FUZZY LOGIC ALGORITHM FOR MOBILE ROBOT CONTROL

Viorel Stoian and Cristina Pana

This paper presents a fuzzy control algorithm for the mobile robots that are moving next to the obstacle boundaries avoiding the collisions with these. Four motion cycles (programs) which depend of the proximity levels and which are followed by the mobile robot on the trajectory (P1, P2, P3, and P4) are shown. The directions of the movements inside every cycle for every proximity level that is reached are presented. The sequence of the programs that depend of the proximity levels that are reached is indicated. The motion control algorithm is presented by a logical diagram of the evolution of the functional cycles (programs). The fuzzy rules for evolution (transition) of the programs and for the motion on X-axis and Y-axis respectively are described. Finally, some simulations are presented.

BEHAVIOR ACTIVITY TRACE METHOD - Application to Dead Locks Detection in a Mobile Robot Navigation

Krzysztof Skrzypczyk

In the paper a novel approach to represent a history in a mobile robot navigation is presented. The main assumptions and key definitions of the proposed approach are discussed in this paper. An application of the method to detect a dead lock situations that may occur during the work of reactive navigation systems is presented. The potential field method is used to create an algorithm that gets the robot out of the dead-lock. Simulations that show the effectiveness of the proposed method are also presented.

OBSERVER BASED OPTIMAL CONTROL OF SHIP ELECTRIC PROPULSION SYSTEM

Habib Dallagi, Ali Sghaïer Tlili and Samir Nejim

This paper describes the synthesis of a linear state observer based optimal control of ship electric propulsion using permanent magnet synchronous motor. The proposed approach is used for the ship speed control by measuring the stator current and the motor speed. This strategy of control is made possible by using a ship speed state observer. A numerical simulation study, applied to the global system, has confirmed the efficiency and the good performances of the proposed control law.

MODELING AND OPTIMAL TRAJECTORY PLANNING OF A BIPED ROBOT USING NEWTON-EULER FORMULATION

David Tlalolini, Yannick Aoustin and Christine Chevallereau

The development of an algorithm to achieve optimal cyclic gaits in space for a thirteen-link biped and twelve actuated joints is proposed. The cyclic walking gait is composed of successive single support phases and impulsive impacts with full contact between the sole of the feet and the ground. The evolution of the joints are chosen as spline functions. The parameters to define the spline functions are determined using an optimization under constraints on the dynamic balance, on the ground reactions, on the validity of impact, on the torques and on the joints velocities. The criterion considered is represented by the integral of the torque norm. The algorithm is tested for a biped robot whose numerical walking results are presented.

PROBABILISTIC MAP BUILDING CONSIDERING SENSOR VISIBILITY

Kazuma Haraguchi, Jun Miura, Nobutaka Shimada and Yoshiaki Shirai

This paper describes a method of probabilistic obstacle map building based on Bayesian estimation. Most active or passive obstacle sensors observe only the most frontal objects and any objects behind them are occluded. Since the observation of distant places includes large depth errors, conventional methods which does not consider the sensor occlusion often generate erroneous maps. We introduce a probabilistic observation model which determine the visible objects. We first estimate probabilistic visibility from the current viewpoint by a Markov chain model based on the knowledge of the average sizes of obstacles and free areas. Then the likelihood of the observations based on the probabilistic visibility are estimated and then the posterior probability of each map grid are updated by Bayesian update rule. Experimental results show that more precise map building can be bult by this method. 1 INTRODUCTION A mobiles robot should know its current position and obstacles and free spaces around it for selfnavigation. That problem is known as SLAM ( Simultaneous Localization And Mapping)(Montemerlo and Thrun, 2002; Thrun et al., 2004; Grisetti et al., 2005). SLAM process is divided into two parts: position localization using current observations and the current map, and the map update using the estimated position. This paper discusses the problem of the conventional map update methods, sensor occlusion, and proposes a novel updating method solving it. The positions of landmarks and obstacles should be represented on the map for navigation(Thrun, 2002b). There are two representations available for them: 1. Feature point on the obstacle 2. Obstacle existence on small map grid. Former representation requires feature identification and matching and the feature position is update when new observation is available(Suppes et al., 2001). Latter representation identifies the map grid from which the observation comes. Since laser range sensors, ultrasonic sensors and stereo image sensors can precisely control the direction of transmitting and receiving light or sound, the place from which observation comes is easy to identify. Thus we use the latter map representation here. The obstacle sensor output always includes observation errors. For example the measurement of time of flight of light or sound have errors caused by wave refraction, diffraction and multi-path reflection. Stereo image sensor also has correspondence failures of image features. These errors lead to large errors or ghost observations. Therefore the map building should be established in a probabilistic way based on a certain error distribution model. Here we use a probabilistic occupancy grid map for the map representation, which stores the obstacle existence probability in each map grid. In this representation, the obstacle existence probability of each grid is updated by evaluating the likelihood of obtained observation for the grid and integrate it with its prior probability in the way of Bayesian estimation.

A FUZZY SYSTEM FOR INTEREST VISUAL DETECTION BASED ON SUPPORT VECTOR MACHINE

Eugenio Aguirre, Miguel García-Silvente, Rui Paúl and Rafael Muñoz-Salinas

Despite of the advances achieved in the past years in order to desing more natural interfaces between intelligent systems and humans, there is still a great effort to be done. Considering a robot as an intelligent system, determining the interest of the sourrounding people in interacting with it is an interesting ability to achieve. That information can be used to establish a more natural communication with humans as well as to design more sophisticated policies for resource assignment. This paper proposes a fuzzy system that establishes a level of possibility about the degree of interest that people around the robot have in interacting with it. First, a method to detect and track persons using stereo vision is briefly explained. Once the visible people is spotted, their interest in interacting with the robot is computed by analyzing its position and its level of attention towards the robot. These pieces of information are combined using fuzzy logic. The level of attention of a person is calculated by analyzing the pose of his head that is estimated in real-time by a view based approach using Support Vector Machines (SVM). Although the proposed system is based only on visual information, its modularity and the use of fuzzy logic make it easier to incorporate in the future other sources of information to estimate with higher precision the interest of people. At the end of the paper, some experiments are shown that validate the proposal and future work is addressed.

HIERARCHICAL SPLINE PATH PLANNING METHOD FOR COMPLEX ENVIRONMENTS

Martin Saska, Martin Hess and Klaus Schilling

Path planning and obstacle avoidance algorithms are requested for robots working in more and more complicated environments. Standard methods usually reduce these tasks to the search of a path composed from lines and circles or the planning is executed only with respect to a local neighborhood of the robot. Sophisticated techniques allow to find more natural trajectories for mobile robots, but applications are often limited to the offline case. The novel hierarchical method presented in this paper is able to find a long path in a huge environment with several thousand obstacles in real time. The solution, consisting of multiple cubic splines, is optimized by Particle Swarm Optimization with respect to execution time and safeness. The generated spline paths result in smooth trajectories which can be followed effectively by nonholonomic robots. The developed algorithm was intensively tested in various simulations and statistical results were used to determine crucial parameters. Qualities of the method were verified by comparing the method with a simple PSO path planning approach.

HELPING INSTEAD OF REPLACING - Towards A Shared Task Allocation Architecture

Foad Ghaderi and Majid Nili Ahmadabadi

Sensitivity of robots to the common mechanical and electrical failures restricts their wide usage in real world applications. Sometimes these failures cause the robots to loss some portion of their capabilities, so that they cannot perform their assigned tasks. Furthermore increases in task complexity yields situations where a single robot can not complete a task, or in some missions none of the existing robots can perform a task individually due to their capabilities limits. In such cases, a team can use redundancy in the number and in the capabilities of robots to help its members and complete the mission by assigning a task to more than one robot. Considering requirements of typical robotic teams during different missions, a distributed behavior based control architecture for cooperative robots is introduced in this paper. This architecture is based on an enhanced version of ALLIANCE, and provides the robots the ability of performing shared tasks based on help requests. The architecture contains a mechanism for adaptive action selection and a communication protocol for information and task sharing which are required for coordination of team members. The proposed architecture is used in a box pushing mission where heterogeneous robots push several boxes with different masses. Simulations show that the team of robots can tolerate faults in its members and performs its mission successfully.

MULTI AGENT-BASED ON-LINE DIAGNOSTIC SCHEME OF SUBSTATION IED

Seong-Jeong Rim and Seung-Jae Lee

This paper presents a novel multi agent-based diagnostic scheme of substation IED. The proposed method is the on-line diagnostic scheme for the IEDs in IEC61850-based SA system using multi-agents. Multi-agents detect the fault of IED installed in substation and apply to improvement of reliability in protection and control system of substation. These agents in the SA system embedded in the station unit, the engineering unit, the trouble manager, backup IED and protection and control IED as the thread form. Through the implementation and the case studies of the proposed scheme, the availability is proven.

ELECTRO HYDRAULIC PRE-ACTUATOR MODELLING OF AN HYDRAULIC JACK

Salazar Garcia Mahuampy, Viot Philippe and Nouillant Michel

Before the realization of testing devices such as a high speed (5m/s) hydraulic hexapod with a high load capacity (60 tons and 6 tons for static and dynamic operating mode), the simulation is an essential step. Hence from softwares such as SimMecanics, we have performed an electro hydraulic model of the servo-valve-jack part by using parameters and recorded results with mono axis testing bench of high-speed hydraulic jack (5m/s), which has a high loading capacity (10 tons for static and 1 ton for dynamic operating mode). The high-speed jack is provided by two parallel three-stage servo-valves. Each three-stages servo-valve supplies 600L/mm. Therefore the unit allows us to obtain a realistic model of an extrapolated hexapod from the mono axis currently used. The aim of this article is to provide a modeling of the second and third stage servo valves by comparison of the typical experimental reading and the computed curves obtained from simulation.

MULTIPLE MODEL ADAPTIVE EXTENDED KALMAN FILTER FOR THE ROBUST LOCALIZATION OF A MOBILE ROBOT

Y. Touati, Y. Amirat, Z. Djama and A. Ali Chérif

This paper focuses on robust pose estimation for mobile robot localization. The main idea of the approach proposed here is to consider the localization process as a hybrid process which evolves according to a model among a set of models with jumps between these models according to a Markov chain. In order to improve the robustness of the localization process, an on line adaptive estimation approach of noise statistics (state and observation), is applied for each mode. To demonstrate the validity of the proposed approach and to show its effectiveness, we’ve compared it to the standard approaches. For this purpose, simulations were carried out to analyze the performances of each approach in various scenarios.

INTERCEPTION AND COOPERATIVE RENDEZVOUS BETWEEN AUTONOMOUS VEHICLES

Yechiel J. Crispin and Marie-Emmanuelle Ricour

The rendezvous problem between autonomous vehicles is formulated as an optimal cooperative control problem with terminal constraints. Traditionally, optimal control problems have been solved by seeking solutions which satisfy the first order necessary conditions for an optimum. Such an approach is based on a Hamiltonian formulation, which leads to a difficult two-point boundary-value problem. We propose a different approach in which the control history is found directly by a genetic algorithm search method. The main advantage of the method is that it does not require the development of a Hamiltonian formulation and consequently, it eliminates the need to deal with an adjoint problem, which usually leads to a difficult two-point boundary-value problem in nonlinear ordinary differential equations. This method has been applied to the solution of both interception and rendezvous problems in an underwater environment, where the direction of the thrust and the velocity vector is used as the control. The method is first tested on an interception chaser-target problem where the passive target vehicle moves along a straight line trajectory at constant speed. We then treat a cooperative rendezvous problem between two active autonomous vehicles. We take into account the effects of gravity, thrust and viscous drag and treat the rendezvous location as a terminal constraint.

TRACKING CONTROL OF WHEELED MOBILE ROBOTS WITH A SINGLE STEERING INPUT - Control Using Reference Time-Scaling

Bálint Kiss and Emese Szádeczky-Kardoss

This paper presents a time-scaling based control strategy of the kinematic model of wheeled mobile robots with one input which is the steering angle. The longitudinal velocity of the mobile robot cannot be influenced by the controller but can be measured. Using an on-line time-scaling, driven by the longitudinal velocity of the robot and its time derivatives, one can achieve exponential tracking of any sufficiently smooth reference trajectory with non-vanishing velocity. The price to pay is the modification of the traveling time along the reference trajectory according to the time-scaling. The measurement of the time derivatives of the velocity is no longer necessary if the tracking controller is designed to the linearized tracking error dynamics.

MINIMIZATION OF l2-SENSITIVITY FOR 2-D SEPARABLE-DENOMINATOR STATE-SPACE DIGITAL FILTERS SUBJECT TO l2-SCALING CONSTRAINTS USING A LAGRANGE FUNCTION AND A BISECTION METHOD

Takao Hinamoto, Yukihiro Shibata and Masayoshi Nakamoto

The problem of minimizing L2-sensitivity subject to L2-scaling constraints for two-dimensional (2-D) separable-denominator state-space digital filters is investigated. The coefficient sensitivity of the filter is analized by using a pure L2-norm. An iterative algorithm for minimizing an L2-sensitivity measure subject to L2-scaling constraints is then explored by introducing a Lagrange function and utilizing an efficient bisection method. A numerical example is also presented to illustrate the utility of the proposed technique.

PROCESS CONTROL USING CONTROLLED FINITE MARKOV CHAINS WITH AN APPLICATION TO A MULTIVARIABLE HYBRID PLANT

Enso Ikonen

Predictive and optimal process control using finite Markov chains is considered. A basic procedure is outlined, consisting of discretization of plant input and state spaces; conversion of a (a priori) plant model into a set of finite state probability transition maps; specification of immediate costs for state-action pairs; computation of an optimal or a predictive control policy; and, analysis of the closed-loop system behavior. An application, using a MATLAB toolbox developed for MDP-based process control design, illustrates the approach in the control of a multivariable plant with both discrete and continuous action variables. For problems of size of practical significance (thousands of states), computations can be performed on a standard office PC. The aim of the work is to provide a basic framework for examination of nonlinear control, emphasizing in on-line learning from uncertain data.

FAST ESTIMATION FOR RANGE IDENTIFICATION IN THE PRESENCE OF UNKNOWN MOTION PARAMETERS

Lili Ma, Chengyu Cao, Naira Hovakimyan, Craig Woolsey and Warren E. Dixon

A fast adaptive estimator is applied to the problem of range identification in the presence of unknown motion parameters. Assuming a rigid-body motion with unknown constant rotational parameters but known translational parameters, extraction of the unknown rotational parameters is achieved by recursive least square method. Simulations demonstrate the superior performance of fast estimation in comparison to identifier based observers.

VERSATILE EVALUATION OF EFFECTS ON DCT-BASED LOSSY COMPRESSION OF EMG SIGNALS ON MEDICAL PARAMETERS

Tiia Siiskonen, Tapio Grönfors and Niina Päivinen

Typically used simplified error measures, like mean-squared-error (MSE), do not reveal everything about the clinical quality of lossy compressed medical signals. Errors have to be interpreted via essential medical parameters. The medical parameters depend on the type of the signal and only the preservation of essential medical parameters can guarantee the correct clinical quality. In this study, short electromyography (EMG) signals are compressed with DCT transformation -based lossy compression method. The compression is gained with irreversible masking and scalar quantization of the DCT coefficients. The most prominent medical parameters of EMG signal are the mean frequency (MNF) and the median frequency (MDF). The behaviors of these parameters are studied both by fitting a regression line and by examining the mean absolute errors frequency-by-frequency over clinically interesting frequency range. This reveals the frequency dependency of errors of the medical parameters and inspires the idea that the generated linear model can be used for estimating the correct value of the processed medical parameter.

BLIND TWO-THERMOCOUPLE SENSOR CHARACTERISATION

Peter C. Hung, Seán F. McLoone, George W. Irwin and Robert J. Kee

Thermocouples are one of the most popular devices for temperature measurement in many mechatronic implementations. However, large wire diameters are required to withstand harsh environments and consequently the sensor bandwidth is reduced. This paper describes a novel algorithmic compensation technique based on blind deconvolution to address this loss of high frequency signal components using the outputs from two thermocouples. In particular, a cross-relation blind deconvolution for parameter estimation is proposed. A feature of this approach, unlike previous methods, is that no a priori assumption is made about the time constant ratio of the two thermocouples. The advantages, including small estimation variance, are highlighted using results from simulation studies.

DIRECTIONAL CHANGE AND WINDUP PHENOMENON

Dariusz Horla

The paper addresses two inherently connected problems, namely: windup phenomenon and directional change in controls problem for multivariable systems. By comparing two ways of performing anti-windup compensation and two different saturation modes a new definition of windup phenomenon for multivariable systems has been obtained, changing definitions present in the literature. It has been shown that avoiding directional change does not have necessarily to mean that windup phenomenon has been avoided too.

IMPROVED ROBUSTNESS OF MULTIVARIABLE MODEL PREDICTIVE CONTROL UNDER MODEL UNCERTAINTIES

Cristina Stoica, Pedro Rodríguez-Ayerbe and Didier Dumur

This paper presents a state space methodology for enhancing the robustness of MIMO MPC controlled systems through the convex optimization of a multivariable Youla parameter. The procedure starts with the design of an initial MPC controller in the state space representation, which is then robustified against modelling errors considered as unstructured uncertainties. The resulting robustified MIMO controller is finally applied to the control of stirred tank reactor to reduce the impact of measurement noise and modeling errors on the system.

WEBMATHEMATICA BASED TOOLS FOR NONLINEAR CONTROL SYSTEMS

Heli Rennik, Maris Tõnso and Ülle Kotta

Algebraic approach of differential one-forms provides simple theoretical framework for several typical problems of nonlinear control theory that makes it useful for educational purposes. Additional assistance is provided by Mathematica functions, developed by us and made available by creating a web-based application using web-Mathematica. These symbolic computation tools provide solutions for several modelling and anlysis problems like accessibility, identifiability, realizability and realization and require no other software except for an internet browser.

THE STRATEGIC GAMES MATRIX AS A FRAMEWORK FOR INTELLIGENT AUTONOMOUS AGENTS HIERARCHICAL CONTROL STRATEGIES MODELING

Eliezer Arantes da Costa and Celso Pascoli Bottura

This paper presents a framework for strategy formulation in multilevel multiple-agent control system architectures based on the Strategic Games Matrix (SGM), having game theory and control systems theory as basic concepts and models. New methodologies for analysis and for design of hierarchical control architectures with multiple intelligent autonomous agents, based on the SGM concept, are proposed. Illustrative hierarchical control applications to system architectures analysis and synthesis based on the SGM are presented.

STABILIZATION OF UNCERTAIN NONLINEAR SYSTEMS VIA PASSIVITY FEEDBACK EQUIVALENCE AND SLIDING MODE

Rafael Castro-Linares and Alain Glumineau

In this paper, a sliding mode controller based on passivity feedback equivalence is developed in order to stabilize an uncertain nonlinear system. It is shown, in particular, that if the nominal passive system obtained by feedback equivalence is asymptotically stabilized by output feedback, then the uncertain system remains stable provided the upper bounds of the uncertain terms are known. The results obtained are applied to the model of a magnetic levitation system to show the controller methodology design.

DUAL CONTROLLERS FOR DISCRETE-TIME STOCHASTIC AMPLITUDE-CONSTRAINED SYSTEMS

A. Królikowski and D. Horla

The paper considers a suboptimal solution to the dual control problem for discrete-time stochastic systems in the case of amplitude constraint imposed on the control signal. The objective of the control is to minimize the variance of the output around the given reference sequence. The presented approaches are based on: an MIDC (Modified Innovation Dual Controller) derived from an IDC (Innovation Dual Controller), a TSDSC (Two-stage Dual Suboptimal Control, and a PP (Pole Placement) controller. Finally, the certainty equivalence (CE) control method is included for comparative analysis. In all algorithms, the standard Kalman filter equations are applied for estimation of the unknown system parameters. Example of second order system is simulated in order to compare the performance of control methods. Conclusions yielded from simulation study are given.

STATE ESTIMATION OF NONLINEAR DISCRETE-TIME SYSTEMS BASED ON THE DECOUPLED MULTIPLE MODEL APPROACH

Rodolfo Orjuela, Benoît Marx, José Ragot and Didier Maquin

Multiple model approach is a powerful tool for modelling nonlinear systems. Two structures of multiple models can be distinguished. The first structure is characterised by decoupled submodels, i.e. with no common state (decoupled multiple model), in opposition to the second one where the submodels share the same state (Takagi-Sugeno multiple model). A wide number of research works investigate the state estimation of nonlinear systems represented by a classic Takagi-Sugeno multiple model. On the other hand, to our knowledge, the state estimation based on the decoupled multiple model has not been investigated extensively. This paper deals with the state estimation of nonlinear systems represented by a decoupled multiple model. Conditions for ensuring the convergence of the estimation error are formulated in terms of a set of Linear Matrix Inequalities (LMIs) employing the Lyapunov direct method.

CLEAR IMAGE CAPTURE - Active Cameras System for Tracking a High-speed Moving Object

Hiroshi Oike, Haiyuan Wu, Chunsheng Hua and Toshikazu Wada

In this paper, we propose a high-performance object tracking system for obtaining high-quality images of a high-speed moving object at video rate by controlling a pair of active cameras that consists of two cameras with zoom lens mounted on two pan-tilt units. In this paper, ``high-quality image'' implies that the object image is in focus and not blurred, the S/N ratio is sufficiently high, the size of the object in the image remains unchanged, and the object is located at the image center. To achieve our goal, we use the K-means tracker algorithm for tracking objects in an image sequence captured by the active cameras. We use the results of the K-means tracker to control the angular position and speed of each pan-tilt-zoom unit by employing the PID control scheme. By using two cameras, the binocular stereo vision algorithm can be used to obtain the 3D position and velocity of the object. These results are used in order to adjust the focus and zoom. Moreover, our system allows the two cameras to gaze at a single point in 3D space. However, this system may become unstable when the time response deteriorates by excessively interfering in a mutual control loop or by strict restriction of the camera action. In order to solve these problems, we introduce the concept of reliability into the K-means tracker, and propose a method for controlling the active cameras by using relative reliability. We have developed a prototype system and confirmed through extensive experiments that we can obtain focused and motion-blur-free images of a high-speed moving object at video rate.

TRANSFORMATION ANALYSIS METHODS FOR THE BDSPN MODEL

Karim Labadi, Haoxun Chen and Lionel Amodeo

The work of this paper contributes to the structural analysis of batch deterministic and stochastic Petri nets (BDSPNs). The BDSPN model is a class of Petri nets introduced for the modelling, analysis and performance evaluation of discrete event systems with batch behaviours. The model is particularly suitable for the modelling of flow evolution in discrete quantities (batches of variable sizes) in a system with activities performed in batch modes. In this paper, transformation procedures for some subclasses of BDSPN are developed and the necessity of the introduction of the new model is demonstrated.

MODIFIED MODEL REFERENCE ADAPTIVE CONTROL FOR PLANTS WITH UNMODELLED HIGH FREQUENCY DYNAMICS

L. Yang, S. A. Neild and D. J. Wagg

In this paper we develop a modified MRAC strategy for use on plants with unmodelled high frequency dynamics. The MRAC strategy is made up of two parts, an adaptive control part and a fixed gain control part. The adaptive algorithm uses a combination of low and high pass filters such that the frequency range for the adaptive part of the strategy is limited. This reduces adaptation to unexpected high frequency dynamics and removes low frequency gain wind-up. In this paper we consider two examples of plants with unmodelled high frequency dynamics, both of which exhibit unstable behaviour when controlled using the standard MRAC strategy. By using the modified strategy we demonstrate that robustness is significantly improved.

FAULT DETECTION ALGORITHM USING DCS METHOD COMBINED WITH FILTERS BANK DERIVED FROM THE WAVELET TRANSFORM

Oussama Mustapha, Mohamad Khalil, Ghaleb Hoblos, Houcine Chafouk and Dimitri Lefebvre

The aim of this paper is to detect the faults in industrial systems, such as electrical machines and drives, through on-line monitoring. The faults that are concerned correspond to changes in frequency components of the signal. Thus, early fault detection, which reduces the possibility of catastrophic damage, is possible by detecting the changes of characteristic features of the signal. This approach combines the Filters Bank technique, for extracting frequency and energy characteristic features, and the Dynamic Cumulative Sum method (DCS), which is a recursive calculation of the logarithm of the likelihood ratio between two local hypotheses. The main contribution is to derive the filters coefficients from the wavelet in order to use the filters bank as a wavelet transform. The advantage of our approach is that the filters bank can be hardware implemented and can be used for online detection.

DESIGN AND IMPLEMENTATION OF A MONITORING SYSTEM USING GRAFCET

Adib Allahham and Hassane Alla

A monitoring system based on a stopwatch automaton is proposed to detect the system faults as early as possible. Each location in the automaton corresponds to a system’s situation. Its time space delimits exactly the range of the normal behavior in the corresponding system’s situation. The monitoring system detects a fault when the time space corresponding to the actual system’s situation is violated. The stopwatch automaton provides a formal foundation to model the system’s behavior and to synthesize the exactly time space in each location. This paper aims to provide the grafcet monitor that allows to link the design of the monitoring system of a system with its implementation in a programmable logic controller. It describes the rules to translate systematically a monitoring stopwatch automaton into a grafcet model. We show that this grafcet fulfils not only the sequential specifications of the dynamic behavior but also the continuous behavior specified by differential equations in the stopwatch automaton. The grafcet monitor models a location by a step and a stopwatch by a timer. Then, it is completed by actions in a way to describe the behavior of the original automaton model. It role is to observe permanently in real time the consistency of the values of stopwatches with its normal range.

IMPEDANCE MATCHING CONTROLLER FOR AN INDUCTIVELY COUPLED PLASMA CHAMBER - L-type Matching Network Automatic Controller

Giorgio Bacelli, John V. Ringwood and Petar Iordanov

Plasma processing is used in a variety of industrial systems, including semiconductor manufacture (deposition and etching) and accurate control of the impedance matching network is vital if repeatable quality is to be achieved at the manufacturing process output. Typically, impedance matching networks employ series (tune) and parallel (load) capacitors to drive the reflection coefficient on the load side of the network to zero. The reflection coefficient is normally represented by real and imaginary parts, giving two variables to be controlled using the load and tune capacitors. The resulting problem is therefore a nonlinear, multivariable control problem. Current industrial impedance matching units employ simple single-loop proportional controllers, which take no account of interaction between individual channels and, in many cases, may fail to tune altogether, if the starting point is far away from the matching point. A hierarchical feedback controller is developed which, at the upper level, performs a single-loop tuning, but with the important addition of a variable sign feedback gain. When convergence to a region in the neighbourhood of the matching point is achieved, a dual single-loop controller takes over, which gives fine tuning of the matching network.

USING NOISE TO IMPROVE MEASUREMENT AND INFORMATION PROCESSING

Solenna Blanchard, David Rousseau and François Chapeau-Blondeau

This paper proposes a synthetic presentation on the phenomenon of stochastic resonance or improvement through the action of noise. Several situations and mechanisms are reported, demonstrating a constructive role of noise, in the context of measurement and sensors, data and information processing, with examples on digital images.

MULTIPLE-MODEL DEAD-BEAT CONTROLLER IN CASE OF CONTROL SIGNAL CONSTRAINTS

Emil Garipov, Teodor Stoilkov and Ivan Kalaykov

The task of achieving a dead-beat control by a linear DB controller under control constraints is presented in this paper. Two algorithms using the concept of multiple-model systems are proposed and demonstrated – a multiple-model dead-beat (MMDB) controller with varying order using one sampling period and a MMDB controller with fixed order using several sampling periods. The advantages and disadvantages of these controllers are summarized

DESIGN OF AN AUTOMATED FIXED BED REACTOR USED FOR A CATALYTIC WET OXIDATION PROCESS

A. El Khoury, B. Bejjany, M. Debacq and A. Delacroix

Treatment of polluted industrial wastes is one of the challenging research topics that occupy an important position in various chemical processes. Wet Air Oxidation (WAO) is one of the emerging processes suited for the treatment of special aqueous wastes. The system consists of an oxidation in the liquid phase of the organic matter by molecular oxygen at high temperature (200-325°C) and high pressure (up to 175 bar). It is an enclosed process with a limited interaction with the environment as opposed to incineration. In this paper, we will discuss the setup and the design of an automated fixed bed reactor used for wet oxidation of various types of wastes. The system is controlled by a set of intelligent sensor modules used for data acquisition. Regulation loops integrated within the sensor modules had been developed in order to control the gas flow, the reactor temperature and the liquid sampling part. The process supervision and monitoring had been achieved through the deployment of a SCADA software application. The graphical interface developed for this purpose monitors the major parts of the process.

A KALMAN FILTERING APPROACH TO ESTIMATE CLAMP FORCE IN BRAKE-BY-WIRE SYSTEMS

Stephen Saric and Alireza Bab-Hadiashar

Removing a clamp force sensor from brake-by-wire (BBW) system designs has been driven by the need to reduce costs and design complexities. In this paper an improved method is presented to estimate clamp force using other sensory information. The proposed estimator is based on the extended Kalman filter (EKF) where the actuator resolver is used in a dynamic stiffness model and the actuator current sensors as well as the resolver are used to give measurement updates in a torque balance model. Experimental results show that the estimator can handle highly dynamic braking scenarios making it suitable for possible use in anti-lock braking system (ABS) controls. A comparison is made with a previous attempt to estimate clamp force in BBW systems and it is shown that the proposed estimator improves the root mean square error (RMSE) of estimation. A training strategy is explained to ensure that the estimator can adequately adapt to parameter variations associated with wear. This paper finally discusses reliability issues associated with the developed clamp force estimator.

A NEW UART CONTROLLER

Nonel Thirer and Radu Florescu

The paper presents a new UART (Universal Asynchronous Receiver/Transmitter) controller which differs from traditional UARTs by providing a user defined data path width of 8 ,16, or 32 bits; by using a one bit error detection and correction algorithm (Hamming); and by permitting a large range of baud rates without the need of adding chips. By using the Hamming code, the communication throughput is increased, especially when a large data path width is defined. This new UART better responds to modern microprocessors’ requirements, and was successfully implemented in an FPGA circuit.

EXPLICIT PREDICTIVE CONTROL LAWS - On the Geometry of Feasible Domains and the Presence of Nonlinearities

Sorin Olaru, Didier Dumur and Simona Dobre

This paper proposes a geometrical analysis of the polyhedral feasible domains for the predictive control laws under constraints. The fact that the system dynamics influence the topology of such polyhedral domains is well known from the studies dedicated to the feasibility of the control laws. Formally the system state acts as a vector of parameters for the optimization problem to be solved on-line and its influence can be fully described by the use of parameterized polyhedra and their dual constraints/generators representation. Problems like the constraints redundancy or the construction of the associated explicit control laws at least for linear or quadratic cost functions can thus receive fully geometrical solutions. Convex nonlinear constraints can be approximated using a description based on the parameterized vertices. In the cas of nonconvex regions the explicit solutions can be obtain by constructing Voronoi partitions based on a collection of points distributed over the borders of the feasible domain.

ON THE JOINT ESTIMATION OF UNKNOWN PARAMETERS AND DISTURBANCES IN LINEAR STOCHASTIC TIME-VARIANT SYSTEMS

Stefano Perabò and Qinghua Zhang

Motivated by fault detection and isolation problems, we present an approach to the design of unknown parameters and disturbances estimators for linear time-variant stochastic systems. The main features of the proposed method are: (a) the joint estimation of parameters and disturbances can be carried out; (b) it is a full-stochastic approach: the unknown parameters and disturbances are random quantities and prior information, in terms of means and covariances, can be easily taken into account; (c) the estimator structure is not fixed a priori, rather derived from the optimal infinite dimensional one by means of a sliding window approximation. The advantages with respect to the widely used parity space approach are presented.

DESIGN AND IMPLEMENTATION OF AN FPGA-BASED SVPWM IC FOR PWM INVERTERS

Cheng-Hung Tsai and Hung-Ching Lu

This paper presents a new circuit design scheme of the space-vector pulse-width modulation (SVPWM) strategy, including linear and overmodulation ranges. The proposed scheme has been developed using the state-of-the-art field-programmable gate array (FPGA) technology. The SVPWM control integrated circuit (IC) can be realized by using only a single FPGA (Cyclone) from Altera, Inc. Experimental results show that this controller can present an excellent drive performance and its switching frequency, which can be set to over 100kHz, is adjustable as well as its deadtime. The output fundamental frequency can be adjusted over 2000Hz. This SVPWM IC can be included in the digital current control loop for stator current regulation. The IC also provide a simple hardware and low cost for high-performance ac drives.

APPLICATION OF SPATIAL H∞ CONTROL TECHNIQUE FOR ACTIVE VIBRATION CONTROL OF A SMART BEAM

Ömer Faruk Kircali, Yavuz Yaman, Volkan Nalbantoğlu, Melin Şahin and Fatih Mutlu Karada

This study presents the design and implementation of a spatial Hinf controller for the active vibration control of a cantilevered smart beam. The smart beam consists of a passive aluminum beam (507x51x2mm) and eight symmetrically surface bonded SensorTech BM500 type PZT (Lead-Zirconate-Titanate) patches (25x20x0.5mm). PZT patches are used as actuators and a laser displacement sensor (Keyence LB-1201(W) LB-300) is used as sensor. The smart beam was analytically modelled by using the assumed-modes method. The model only included the first two flexural vibrational modes and the model correction technique was applied to compensate the possible error due to the out of range modes. The system model was also experimentally identified and both theoretical and experimental models were used together in order to determine the modal damping ratios of the smart beam. A spatial Hinf controller was designed for the suppression of the vibrations of the smart beam due to its first two flexural modes. The designed controller was then implemented and the suppression of the vibrations was experimentally demonstrated. This study also compared the effectiveness of a pointwise controller with the new spatial one.

CONJUGATE GRADIENT TECHNIQUES FOR MULTICHANNEL ADAPTIVE FILTERING

Lino García Morales and Fernando Juan Berenguer Císcar

The conjugate gradient is the most popular optimization method for solving large systems of linear equations. In a system identification problem, for example, where very large impulse response can be involucrate, it is necessary to apply a particular strategy in order to diminish the delay while speed up the convergence time. In this paper we propose a new scheme which combine frequency-domain adaptive filtering with conjugate gradient technique in order to solve a big order multichannel adaptive filter while delayless and very short convergence time are guaranty.

MULTICHANNEL FILTER FOR ENHANCEMENT OF SPEECH BLOCKS

Ivandro Sanches

This work presents the concepts and the achieved results of a proposed microphone array algorithm based on multi-dimensional Wiener filter developed to work on blocks of speech. The inputs to the algorithm are two correlation matrices: the correlation matrix of the background noise affecting the desired signal and the correlation matrix of the signal affected by the noise. Experiments show that improvements of more than 12dB on signal to noise ratio can be achieved when comparing the filtered signals with one of the microphone array channels. In order to save computational load, the input signal is processed in blocks of a specified size and a technique is proposed to reduce blocking effects on the output filtered signal. It will be shown that practically there are no blocking effects. It is also shown that the technique is independent of the array physical configuration.

SEARCHING AND FITTING STRATEGIES IN ACTIVE SHAPE MODELS

Jianhua Zhang, S. Y. Chen, Sheng Liu, Qiu Guan and Haihong Wu

The Active Shape Model (ASM) is an ever-increasingly important method for object modeling, shape recognition, and image localization. In this paper, two strategies are proposed on the ASM searching and fitting procedure, which forms an active searching method. The first is the indirect self-adjustment of shape parameters, i.e. the vector b, according to the minimized square error (MSE) to meet the target shape points. Secondly, some outlying points which are determined by the MSE criterion are excluded for avoiding deformation in a bad direction of the model shape. Experiments and results show that these strategies are effective for improving the performance of the image fitting.

PROGRESSES IN CONTINUOUS SPEECH RECOGNITION BASED ON STATISTICAL MODELLING FOR ROMANIAN LANGUAGE

Corneliu Octavian Dumitru, Inge Gavat and Diana Militaru

In this paper we will present progresses made in Automatic Speech Recognition (ASR) for Romanian language based on statistical modelling with hidden Markov models (HMMs). The progresses concern enhancement of modelling by taking into account the context in form of triphones, improvement of speaker independence by applying a gender specific training and enlargement of the feature categories used to describe speech sequences derived not only from perceptual cepstral analysis but also from perceptual linear prediction.

STUDY OF A CONTROLED COMPLEX MECHANICAL SYSTEM IN ANTI VIBRATORY DOMAIN - Application to a Hard Landing of an Aircraft

Cédric Lopez, François Malburet and André Barraco

This paper studies problematic of a mechanical system composed of different parts mechanically coupled and submitted to a high speed shock. After a shock, different parts of the system oscillate. If one of them is excited at a particular frequency, such as its proper frequency, important oscillations appear and can lead to the deterioration of the system by introducing important stresses. In this paper, we propose an analysis in order to understand this kind of problem and what we can do to avoid it. Firstly we discuss problematic and we expose the studied system. In a second time, we present model which allows us to understand the phenomenon by carrying out numerical simulations. Then we complete a comparative analysis of different methods of control. Prospects and problematic of real controlled device are studied. Finally experimental set up is described.

A MULTI-MODEL APPROACH FOR BILINEAR GENERALIZED PREDICTIVE CONTROL

Anderson Luiz de Oliveira Cavalcanti, André Laurindo Maitelli and Adhemar de Barros Fontes

This paper presents a contribution in multivariable predictive control. A new approach of multi-model based control is presented. The controller used is the quasilinear multivariable generalized predictive control (QMGPC). A metric based in 2-norm is presented in order to build a global model using local models. Simulation results in a distillation column, with a comparative analysis, are presented.

APPLICATIONS OF A MODEL BASED PREDICTIVE CONTROL TO HEAT-EXCHANGERS

Radu Bălan, Vistrian Mătieş, Victor Hodor, Sergiu Stan, Ciprian Lăpuşan and Horia Bălan

Model based predictive control (MBPC) is an optimization-based approach that has been successfully applied to a wide variety of control problems. When MBPC is employed on nonlinear processes, the application of this typical linear controller is limited to relatively small operating regions. The accuracy of the model has significant effect on the performance of the closed loop system. Hence, the capabilities of MBPC will degrade as the operating level moves away from its original design level of operation. This paper presents an MPC algorithm which uses on-line simulation and rule-based control. The basic idea is the on-line simulation of the future behaviour of control system, by using a few control sequences and based on nonlinear analytical model equations. Finally, the simulations are used to obtain the „optimal‟ control signal. These issues will be discussed and nonlinear modeling and control of a single-pass, concentric-tube, counter flow or parallel flow heat exchanger will be presented as an example.

DECENTRALIZED APPROACH FOR FAULT DIAGNOSIS OF DISCRETE EVENT SYSTEMS

Moamar Sayed Mouchaweha, Alexandre Philippotb and Véronique Carré-Ménétriera

This paper proposes a decentralized approach to realize the diagnosis of Discrete Event Systems (DES). This approach is based on a set of local diagnosers, each one of them diagnoses faults entailing the violation of the local desired behaviour. These local diagnosers infer the fault’s occurrence using event sequences, time delays between correlated events and state conditions, characterized by sensors readings and commands issued by the controller. An adapted codiagnosability notion is formally defined in order to ensure that the set of local diagnosers is able to diagnose all faults entailing the violation of the global desired behavior. An example is used to illustrate the proposed approach.

TRACKING PLASMA ETCH PROCESS VARIATIONS USING PRINCIPAL COMPONENT ANALYSIS OF OES DATA

Beibei Ma, Seán McLoone and John Ringwood

This paper explores the application of principal component analysis (PCA) to the monitoring of within-lot and between-lot plasma variations that occur in a plasma etching chamber used in semiconductor manufacturing, as observed through Optical Emission Spectroscopy (OES) analysis of the chamber exhaust. Using PCA, patterns that are difficult to identify in the 2048-dimension OES data are condensed into a small number of principle components (PCs). It is shown, with the aid of experimental data, that by simply tracking changes in the directions of these PCs both inter-lot and intra-lot patterns can be identified.

IMPLEMENTATION OF RECURRENT MULTI-MODELS FOR SYSTEM IDENTIFICATION

Lamine Thiaw, Kurosh Madani, Rachid Malti and Gustave Sow

Multi-modeling is a recent tool proposed for modeling complex nonlinear systems by the use of a combination of relatively simple set of local models. Due to their simplicity, linear local models are mainly used in such structures. In this work, multi-models having polynomial local models are described and applied in system identification. Estimation of model's parameters is carried out using least squares algorithms which reduce considerably computation time as compared to iterative algorithms. The proposed methodology is applied to recurrent models implementation. NARMAX and NOE multi-models are implemented and compared to their corresponding neural network implementations. Obtained results show that the proposed recurrent multi-model architectures have many advantages over neural network models.

INNER AND OUTER APPROXIMATION OF CAPTURE BASIN USING INTERVAL ANALYSIS

Mehdi Lhommeau, Luc Jaulin and Laurent Hardouin

This paper proposes a new approach to solve the problem of computing the capture basin $\mathbb{C}$ of a target $\mathbb{T}$. The capture basin corresponds to the set of initial states such that the target is reached in finite time before possibly leaving of constrained set. We present an algorithm, based on interval analysis, able to characterize an inner and an outer approximation $\mathbb{C}^- \subset \mathbb{C} \subset \mathbb{C}^+$ of the capture basin. The resulting algorithm is illustrated on the Zermelo problem.

SLIDING MODE CONTROL FOR HAMMERSTEIN MODEL BASED ON MPC

Zhiyu Xi and Tim Hesketh

This paper addresses discrete sliding mode control of nonlinear systems. The nonlinear system is identified as a Hammerstein model firstly to isolate the nonlinearity from the sliding surface design. An MPC law is employed to design the sliding surface. Then Utkin's "method of equivalent control" is used. The method illustrates the effect of the nonlinearity on reaching control. The ball and beam system is adopted as an example. Simulation and on-line results are provided.

USE A NEURAL NETWORKS TO ESTIMATE AND TRACK THE PN SEQUENCE IN LOWER SNR DS-SS SIGNALS

Tianqi Zhang, Shaosheng Dai, Zhengzhong Zhou and Xiaokang Lin

This paper proposes a modified Sanger’s generalized Hebbian algorithm (GHA) neural network (NN) method to estimate and track the pseudo noise (PN) sequence in lower signal to noise ratios (SNR) direct sequence spread spectrum (DS-SS) signals. The proposed method is based on eigen-analysis of DS-SS signals. The received signal is firstly sampled and divided into non-overlapping signal vectors according to a temporal window, which duration is a periods of PN sequence. Then an autocorrelation matrix is computed and accumulated by these signal vectors one by one. The PN sequence can be estimated and tracked by the principal eigenvector of autocorrelation matrix in the end. But the eigen-analysis method becomes inefficiency when the estimated PN sequence becomes longer or the estimated PN sequence becomes time varying. In order to overcome these shortcomings, we use a modified Sanger’s GHA NN to realize the PN sequence estimation and tracking from lower SNR input DS-SS signals adaptively and effectively.

GENERAL FORMULATION OF SYSTEM DESIGN PROCESS - Design Process Formulation as a Controllable Dynamic System

Alexander Zemliak and Roberto Galindo-Silva

The formulation of the process of analogue circuit design has been done on the basis of the control theory application. This approach produces the set of different design strategies inside the same optimization procedure. Basic equations for this design methodology were elaborated. The problem of the time-optimal design algorithm construction is defined as the problem of a functional minimization of the optimal control theory. By this context the design process is defined as a controllable dynamic system. Numerical results of some electronic circuit design demonstrate the efficiency of the proposed methodology and prove the non-optimality of the traditional design strategy.

SAFETY VALIDATION OF AUTOMATION SYSTEMS : APPLICATION FOR TEACHING OF DISCRETE EVENT SYSTEM CONTROL

Pascale Marange, François Gellot and Bernard Riera

We propose in this paper, to introduce a method to validate logic control programs adapted to the teaching of DES (Discrete Event Systems). The use of real systems for teaching raises two problems. The first one concerns the security of human beings (students and teachers) and materials. The second problem is the necessity to be able to detect and bring an explanation to possible errors made by students. We propose a method to define a level of system abstraction, to validate the learning’s control while placing a filter from validation between the plant of a system and the control. The specifications contained in the filter make it possible to detect errors and to generate an explanation automatically. We applied this method to an original project where it was proposed to 7 year old children, to discover automation, by programming a pills packaging system.

PRELIMINARY TESTS OF THE REMS GT-SENSOR

Eduardo Sebastián and Javier Gomez-Elvira

This paper describes and tests a mathematical model of the REMS GT-sensor (Ground Temperature), which will be part of the payload of the NASA MSL mission to Mars. A short review of the instrument most critical aspects like the in-flight calibration system and the small size, are presented. It is proposed a mathematical model of the GT-sensor based on an energy balance theory, which considers the internal construction of the thermopile, and allows the designer to model independently the change in any of its parameters. The instrument includes an in-flight calibration system which accounts for dust build up on the thermopile window during operations. Pre-calibration tests of the system are presented, demonstrating the good performance of the proposed model, as well as some required improvements.

ADVANCED CONTROL OF AEROBIC INDUSTRIAL WASTEWATER TREATMENT

Matei Vinatoru, Eugen Iancu, Gabriela Canureci and Camelia Maican

The paper present the possibility of automatic control of the biological wastewater treatment station with applications in Romanian Chemical Companies. In this paper are developed a mathematical model for biological aeration basins and two automatic control systems (conventional control structure using three-positional controllers or PLC and advanced control structure using state estimators) for wastewater industrial purification stations

TIME-FREQUENCY REPRESENTATION OF INSTANTANEOUS FREQUENCY USING A KALMAN FILTER

Jindřich Liška and Eduard Janeček

In this paper, a new method for obtaining a time-frequency representation of instantaneous frequency is introduced. A Kalman filter serves for dissociation of signal into modes with well defined instantaneous frequency. A second order resonator model is used as a model of signal components – ‘monocomponent functions’. Simultaneously, the Kalman filter estimates the time-varying signal components in a complex form. The initial parameters for Kalman filter are obtained from the estimation of the spectral density through the Burg’s algorithm by fitting an auto-regressive prediction model to the signal. To illustrate the performance of the proposed method, example of real application shows the contribution of this method to improve the time-frequency resolution.

HUMAN-SCALE VIRTUAL REALITY CATCHING ROBOT SIMULATION

Ludovic Hamon, François-Xavier Inglese and Paul Richard

Fractal patterns contain rich potentialities to be explored in the context of fashion design. In particular, automatic generation of fractal images based on iterated function systems (IFS) could stimulate creativity and allow fast generation of solutions. However, this relies on efficient user interfaces and interaction techniques. In this paper, we describe and analyze a basic model of IFS and address the issue of controlling the fractal images they generate. We specially focus on fast exploration of the IFS parameters space and aesthetic properties of the generated images through both 2D visualization and 3D mapping on virtual characters.

IMAGE PREPROCESSING FOR CBIR SYSTEM

Tatiana Jaworska

This article describes the way in which image is prepared for content-based image retrieval system. Automated image extraction is crucial; especially, if we take into consideration the fact that the feature selection is still a task performed by human domain experts and represents a major stumbling block on the creation process of fully autonomous CBIR systems. Our CBIR system is dedicated to support estate agents. In the database there are images of houses and bungalows. All efforts have been put into extracting elements from an image and finding their characteristic features in the unsupervised way. Hence, the paper presents segmentation algorithm based on a pixel colour in RGB colour space. Next, it presents the method of object extraction in order to obtain separate objects prepared for the process of introducing them into database and further recognition. Moreover, a novel method of texture identification which is based on wavelet transformation, is applied. Due to the fact that the majority of texture is geometrical (such as bricks and tiles) we have used the Haar wavelet. After a set of low-level features for all objects is computed, the database is stored with these features.

A STATE ESTIMATOR FOR NONLINEAR STOCHASTIC SYSTEMS BASED ON DIRAC MIXTURE APPROXIMATIONS

Oliver C. Schrempf and Uwe D. Hanebeck

This paper presents a filter for estimating the state of nonlinear dynamic systems based on recursive approximation of posterior densities by means of Dirac mixture functions. The filter consists of a prediction step and a filter step. The approximation approach is based on a systematic minimization of a distance measure and is hence optimal and deterministic. This allows for finding an optimal number of components in the Dirac mixture. Further optimization is gained by taking the measurement of the system into account during the approximation process.

SIMULATION AND FORMAL VERIFICATION OF REAL TIME SYSTEMS: A CASE STUDY

Eurico Seabra, José Machado, Jaime Ferreira da Silva, Filomena O. Soares and Celina P. Leão

In this paper will be presented and discussed a case study that apply techniques of simulation together with techniques of formal verification. This is presented a new approach in the plant modelling for formal verification of timed systems. The modelling of the plant was performed using the object-oriented language Modelica with the library for hierarchical state machines StateGraph and the simulation results were used as input for the formal verification tasks, using the model checker UPPAAL. It is presented, in a more detailed way, the part of this work that is related with the plant simulation.

EFFICIENT IMPLEMENTATION OF FAULT-TOLERANT DATA STRUCTURES IN PC-BASED CONTROL SOFTWARE

Michael Short

Recent years have seen an increased interest in the use of open-architecture, PC-based controllers for robotic and mechatronic systems. Although such systems give increased flexibility and performance at low unit cost, the use of commercial processors and memory devices can be problematic from a safety perspective as they lack many of the built-in integrity testing features that are typical of more specialised equipment. Previous research has shown that the rate of undetected corruptions in industrial PC memory devices is large enough to be of concern in systems where the correct functioning of equipment is vital. In this paper the mechanisms that may lead to such corruptions and the level of risk is examined. A simple, portable and highly effective software library is also presented in this paper that can reduce the impact of such memory errors. The effectiveness of the library is verified in a small example.

ROBUST AND STABLE ROBOTIC FORCE CONTROL

Michael Short and Kevin Burn

To perform many complex tasks, modern robots often require robust and stable force control. Linear, fixed-gain controllers can only provide adequate performance when they are tuned to specific task requirements, but if the environmental stiffness at the robot/task interface is unknown or varies significantly, performance is degraded. This paper describes the design of a robotic force controller that has a simple architecture yet is robust to bounded uncertainty in the environmental stiffness. Generic stability conditions for the controller are developed and a simple design methodology is formulated. The controller design is tested on an experimental robot, and is shown to perform favourably in the presence of large changes in environmental operating conditions.

A LOCAL LEARNING APPROACH TO REAL-TIME PARAMETER ESTIMATION - Application to an Aircraft

Lilian Ronceray, Matthieu Jeanneau, Daniel Alazard, Philippe Mouyon and Sihem Tebbani

This paper proposes an approach based upon local learning techniques for real-time parameter estimation, using radial-basis neural networks. Firstly, a formulation of the general problem of locality in real-time estimation is given and is followed by a description of radial-basis networks, using the notion of local models. The application to the estimation of the sideslip angle of an aircraft, is then presented and the various results and analyses are detailled at the end before suggesting some improvement directions.

ROBUST CONTROL OF HYSTERETIC BASE-ISOLATED STRUCTURES UNDER SEISMIC DISTURBANCES

Francesc Pozo, José Rodellar, Leonardo Acho and Ricardo Guerra

The main objective of applying robust control to base-isolated structures is to protect these structures against seismic events. Taking advantage of Lyapunov theory for perturbed systems, a static active control is developed with only two terms. In some cases, this active control can even be reduced to just one term. Robustness performance is analyzed by means of numerical simulations using the 1940 El Centro earthquake, showing that the control energy can be significantly reduced.

ADAPTIVE PREDICTIVE CONTROLLER APPLIED TO AN OPEN WATER CANAL

Luís Rato, Pedro Salgueiro, João Miranda Lemos and Manuel Rijo

This paper concerns to the application of adaptive control to a large scale water canal experimental plant. Water canals are complex spatially distributed systems which aim at distributing water either for irrigating, or domestic, or industrial purposes. In this paper a predictive adaptive control algorithm is applied to a large scale experimental water canal prototype. The experimental water canal facilities are described briefly. This facilities are constituted by a fully instrumented canal, a PLC network and a SCADA system. This paper describes the developed software module and the applied predictive adaptive algorithm, MUSMAR. The ability of this control algorithm to cope with variable time delays makes it a good candidate to this application where time delays as well as other dynamic behavior may vary with operating conditions. Finaly, Some experimental results obtained in the experimental water canal, are presented.

A CLOSED-FORM MODEL PREDICTIVE CONTROL FRAMEWORK FOR NONLINEAR NOISE-CORRUPTED SYSTEMS

Florian Weissel, Marco F. Huber and Uwe D. Hanebeck

In this paper, a framework for Model Predictive Control (MPC) that explicitly incorporates the noise influence on nonlinear systems with continuous state spaces is introduced. By the incorporation of noise, which results from uncertainties during the model identification and the measurement process, the quality of control can be significantly increased. Since nonlinear MPC requires the prediction of system states over a certain horizon, an efficient state prediction technique for nonlinear noise-affected systems is required. This is achieved by using transition densities approximated by axis-aligned Gaussian mixtures together with methods to reduce the computational burden. A versatile cost function representation also employing Gaussian mixtures provides an increased freedom of modeling. Combining the prediction technique with this value function representation allows closed-form calculation of the necessary optimization problems arising from MPC. The capabilities of the framework and especially the benefits that can be gained by incorporating the noise in the controller are illustrated by the example of a mobile robot following a given path.

A SAMPLING FORMULA FOR DISTRIBUTIONS

W. E. Leithead and E. Ragnoli

A key sampling formula for discretising a continuos-time system is proved when the signals space is a subclass of the space of Distributions. The result is applied to the analysis of an open-loop hybrid system.

SMART DIFFERENTIAL PRESSURE SENSOR

Michal Pavlik, Jiri Haze, Radimir Vrba and Miroslav Sveda

This paper describes design, construction and some results of the proposed electronics for processing of the measured signal of the capacitive difference pressure sensor. This intelligent pressure sensor has current loop output and can be used for show current value of the supervised pressure or for automation as a sensing unit. Measured data of the sensor are transmitted via current loop 4-20mA. The sensor is supplied from the current loop, too. This means that current consumption of the whole sensor must be less than 3,5mA in whole temperature range –40 to +125°C.

RUN-TIME RECONFIGURABLE SOLUTIONS FOR ADAPTIVE CONTROL APPLICATIONS

George Economakos, Christoforos Economakos and Sotirios Xydis

The requirement for short time-to-market has made FPGA devices very popular for the implementation of general purpose electronic devices. Modern FPGA architectures offer the advantage of partial reconfiguration, which allows an algorithm to be partially mapped into a small and fixed FPGA device that can be reconfigured at run time, as the mapped application changes its requirements. Such a feature can be beneficial for modern control applications, that may require the change of coefficients, models and control laws with respect to external conditions. This paper presents an embedded run-time reconfigurable architecture and the corresponding design methodologies that support flexibility, modularity and abstract system specification for high performance adaptive control applications. Through experimental results it is shown that this architecture is both technically advanced and cost effective so, it can be used in increasingly demanding application areas like automotive control.

A COMPONENT-BASED APPROACH FOR CONVEYING SYSTEMS CONTROL DESIGN

Jean-Louis Lallican, Pascal Berruet, André Rossi and Jean-Luc Philippe

This paper deals with the design of discrete control for conveying systems. A methodology based on components is introduced to model controlled conveying systems. A component is a reusable element that includes several views including partial models. It is formalized referring to the notion of operations. Four views are delineated in this paper: Operating part view, Constraints view, Graphical view and Control view. The control model of the workshop is built on these views. A methodology allowing to automatically generate the control programs is proposed to provide an easy way to obtain source code compatible with the IEC 61131-3 standard. Its purpose is to automate the development of control programs in order to reduce costs. Tools allowing to implement the methodology are also presented, along with some applications.

GPC BASED ON OPERATING POINT DEPENDENT PARAMETERS LINEAR MODEL FOR THERMAL PROCESS

Riad Riadi, Rousseau Tawegoum, Gérard Chasseriaux and Ahmed Rachid

This paper presents the application of generalized predictive control strategy (GPC) based on an OPDPLM (Operating Point Dependent Parameters Linear Model) structure to a heating and ventilation nonlinear-subsystem of a complex passive air-conditioning unit. For this purpose, several discrete-time models are identified with respect to measurable exogenous events. The parameters of the identified models change according operating conditions (sliding opening window). The objective of the subsystem studied is to guarantee a microclimate with controlled temperature setpoints. In this case a strategy which updates on line the right controller to achieve the same performances for a given objective. Efficiency of the resulting algorithm is illustrated by a real experiment

MECHANICAL SYSTEM MODELLING OF ROBOT DYNAMICS USING A MASS/PULLEY MODEL

L. J. Stocco and M. J. Yedlin

The well-known electro-mechanical analogy that equates current, voltage, resistance, inductance and capacitance to force, velocity, damping, spring constant and mass has a shortcoming in that mass can only be used to simulate a capacitor which has one terminal connected to ground. A new model that was previously proposed by the authors that combines a mass with a pulley (MP) is shown to simulate a capacitor in the general case. This new MP model is used to model the off-diagonal elements of a mass matrix so that devices whose effective mass is coupled between more than one actuator can be represented by a mechanical system diagram that is topographically parallel to its equivalent electric circuit model. Specific examples of this technique are presented to demonstrate how a mechanical model can be derived for both a serial and a parallel robot with both two and three degrees of freedom. The technique, however, is extensible to any number of degrees of freedom.

AN INVESTIGATION OF EXTENDED KALMAN FILTERING IN THE ERRORS-IN-VARIABLES FRAMEWORK - A Joint State and Parameter Estimation Approach

Jens G. Linden, Benoit Vinsonneau and Keith J. Burnham

The paper addresses the problem of errors-in-variables filtering, i.e. the optimal estimation of inputs and outputs from noisy observations. While the optimal solution is known for linear time-varying systems of known parameterisation, this paper considers a suboptimal approach when only an approximated set of parameters is available. The proposed filter is derived by the means of joint state and parameter estimation using the extended Kalman filter approach which, in turns, leads to a coupled state-parameter estimation. However, the resulting parameter estimates appears to be biased in the presence of input noise. The novel filter is compared with a previously proposed suboptimal filter.

BICYCLE WHEEL WOBBLE - A Case Study in Dynamics

John V. Ringwood and Ruijuan Feng

This paper examines reasons why wheel wobble occurs in common production bicycles. In particular, the effects of frame size, rider position and riding style are examined with reference to a range of mathematical models of bicycles which are available in the published literature. Much of the motivation for this work comes from the personal cycling experience of one of the authors and the difficulty in resolving the true cause of wheel wobble from the wide range of advice offered of a variety of cycling experts. It is hoped that recourse to a mathematical analysis will give objective direction as to how wheel wobble can be alleviated through rider intervention.

An Improved Architecture for Cooperative and Comparative Neurons (CCNs) in Neural Network

Md. Kamrul Islam

The ability to store and retrieve information is critical in any type of neural network. In neural network, the memory particularly associative memory, can be defined as the one in which the input pattern leads to the response of a stored pattern (output vector) that corresponds to the input vector. During the learning phase the memory is fed with a number of input vectors that it learns and remembers and in the recall phase when some known input is presented to it, the network exactly recalls and reproduces the required output vector. In this paper, we improve and increase the storing ability of the memory model proposed in \cite{Bar}. Besides, we show that there are certain instances where the algorithm in \cite{Bar} does not produce the desired performance by retrieving exactly the correct vector from the memory. That is, in their algorithm, a number of output vectors can become activated from the stimulus of an input vector while the desired output is just a single correct vector. We propose a simple solution that overcomes this and can uniquely and correctly determine the output vector stored in the associative memory when an input vector is applied. Thus we provide a more general scenario of this neural network memory model consisting of memory element called Competitive Cooperative Neuron (CCN)

Automatic Classification of Spinal Deformity by using Four Symmetrical Features on the Moire Images

Hyoungseop Kim, Satoshi Nakano, Joo Kooi Tan, Seiji Ishikawa, Yoshinori Otsuka, Hisashi Shimizu and Takashi Shinomiya 4

Spinal deformity is a disease mainly suffered by teenagers during their growth stage particularly from element school to middle school. There are many different causes of abnormal spinal curves, but all of them are unknown. The most common type is termed “idiopathic” that show 80% of the spinal de-formity. Spinal deformity is a serious disease, mainly suffered by teenagers, es-pecially girl’s student, during their growth stage.　To find the spinal deformity in early stage, orthopedists have traditionally performed on children a painless examination called a forward bending test in mass screening of school. But this test is neither objective nor reproductive, and the inspection takes much time when applied to medical examination in schools. To solve this problem, a moire method has been proposed which takes moire topographic images of human subject backs and checks symmetry/asymmetry of the moire patterns in a two-dimensional way. In this paper, we propose a method for automatic classifica-tion of spinal deformity from moire topographic images by extracting four sym-metrical features of the left-hand and right-hand side on the moire image. Fea-ture of asymmetry degrees are applied to train employing the classifier such as Artificial Neural Network, Support Vector Machine, Self-Organization Map and AdaBoost.

Direct and Indirect Classification of High-Frequency LNA Performance using Machine Learning Techniques

Peter C. Hung, Seán F. McLoone, Magdalena Sánchez,Ronan Farrell and Guoyan Zhang

The task of determining low noise amplifier (LNA) high-frequency performance in functional testing is as challenging as designing the circuit itself due to the difficulties associated with bringing high frequency signals off-chip. One possible strategy for circumventing these difficulties is to attempt to predict the high frequency performance measures using measurements taken at lower, more accessible, frequencies. This paper investigates the effectiveness of machine learning based classification techniques at predicting the gain of the amplifier, a key performance parameter, using such an approach. An indirect artificial neural network (ANN) and direct support vector machine (SVM) classification strategy are considered. Simulations show promising results with both methods, with SVMs outperforming ANNs for the more demanding classification scenarios.

Initialization by Selection for Multi library Wavelet Neural Network Training

Wajdi bellil1, Mohamed Othmani and Chokri Ben Amar

This paper presents an original architecture of Wavelet Neural Network based on multi Wavelets activation function and uses a selection method to determine a set of best wavelets whose centres and dilation parameters are used as initial values for subsequent training library WNN for one dimension and two dimensions function approximation. Every input vector will be considered as unknown functional mapping and then it will be approximated by the network.

A New Model of Associative Memories Network

Roberto A. Vázquez and Humberto Sossa

An associative memory (AM) is a special kind of neural network that only allows associating an output pattern with an input pattern. However, some problems require associating several output patterns with an only input pattern. Classical associative and neural models cannot solve this simple task. In this paper we propose a new network composed of several AMS aimed to solve this problem. Using this new model the AMS can be able to associate several output patterns with an only input pattern. We test the accuracy of the proposal with a database of real images. We split this database of images into four collections of images and then we trained the network of AMS. During training we associated an image of a collection with the rest of the images belonging to the same collection. Once trained the network we expected to recover a collection of images by using as an input pattern any image belonging to the collection.

Industrial Application Development using Case-based Reasoning

Miroslav Sveda and Ondrej Rysavy

Every design deserves decisions based on the application domain knowledge collected from previous similar implementations. The paper deals with stepwise development of a dedicated LAN-based industrial measurement application. The conception of this development stems from a knowledge preserving, graceful conversion of the original enterprise practice into a co-operative work supporting arrangement. The principal paradigm employed for this conversion is case-based reasoning augmented by rule-based support.

Forming Neural Networks Design through Evolution

Eva Volná

Neuroevolution techniques have been successful in many sequential decision tasks such as robot control and game playing. This paper aims at evolution in artificial neural networks (e.g. neuroevolution). Here is presented a neuroevolution system evolving populations of neurons that are combined to form the fully connected multilayer feedforward network with fixed architecture. In this paper, the transfer function has been shown to be an important part of architecture of the artificial neural network and have significant impact on an artificial neural network’s performance. In order to test the efficiency of described method, we applied it to the alphabet coding problem.

Architectonics of Thinking: The Conception of Human Brain Organization as Multiprocessing System

Valery Shyrochin and Vadym Mukhin

This paper is devoted to the development of hypothesis about human brain “cartation”, i.e. spatially distributed structure of human brain systems for cogitative activities maintenance, containing neural parts with various degree of rigidity and different functional tasks (cartoids), which was suggested by Institute of Brain of the Russian Academy of Sciences (St.-Petersburg) under supervision of acad. N. Bekhtereva. In the paper is suggested the conception of the structurally-logic organization of thinking processes, which defines the main attitudes between the emotional-strong-willed (unconscious) and the intellectual-ethical (conscious) components (cartoids-processors) of thinking for the situation analysis and for the choice of actions ways by the person during decisions making and realization. The conception is based on the architectonics of the artificial intellect of the future generations for creation of the emotionally and morally-oriented knowledge bases and supercomputers, capable to realize all the processes of the productive creative thinking.

Impact of Data Dimensionality Reduction on Neural Based Classification: Application to Industrial Defects

Matthieu Voiry, Kurosh Madani, Véronique Amarger and Joël Bernier

A major step for high-quality optical surfaces faults diagnosis concerns scratches and digs defects characterisation in products. This challenging operation is very important since it is directly linked with the produced optical component’s quality. A classification phase is mandatory to complete optical devices diagnosis since a number of correctable defects are usually present be-side the potential “abiding” ones. Unfortunately relevant data extracted from raw image during defects detection phase are high dimensional. This can have harmful effect on behaviors of artificial neural networks which are suitable to perform such a challenging classification. Reducing data dimension to a smaller value can decrease the problems related to high dimensionality. In this paper we compare different techniques which permit dimensionality reduction and evalu-ated their possible impact on classification tasks performances.

Improved Neural Network-based Face Detection Method using Color Images

Yura Kurylyak, Ihor Paliy, Anatoly Sachenko, Kurosh Madani and Amina Chohra

The paper describes some face detection algorithms using skin color segmentation, Haar-like features and neural networks. The segmentation using skin color labels promising input image areas that may contain faces. The usage of Haar-like features allows fast rejection of the majority of background. Then, the ensemble of retinally connected neural networks performs the final classifi-cation of the rest image windows using improved face search strategy across scale and position. The proposed search strategy applies inverse image scale pyramid, adaptive scanning step and window acceptance to decrease the num-ber of windows which should be processed by the classifier.

ZISC Neural Network Base Indicator for Classification Complexity Estimation

Ivan Budnyk, Abdennasser Сhebira and Kurosh Madani

This paper presents a new approach for estimating task complexity using IBM© Zero Instruction Set Computer (ZISC ©).The goal is to build a neural tree structure following the paradigm “divide and rule”. The aim of this work is to define a complexity indicator-function and to hallmark its’ main features

Noisy Image Processing Using the Independent Component Analysis Algorithm AMUSE

Salua Nassabay, Ingo R. Keck, Carlos G. Puntonet, Juan M. Górriz, J. Pérez de Inestroaa and Rubén M. Clemente

In this article we investigate the performance of the ICA algorithm AMUSE when applied to images contaminated by noise. The classes of noise we are using have gaussian, multiplicative and impulsive distributions. We find that AMUSE copes surprisingly well with the different types of noise, including multiplicative noise.

Fuzzy Clustering Methods in Multispectral Satellite Image Segmentation

Rauf Kh. Sadykhov, Andrey V. Dorogush and Leonid P. Podenok

Segmentation method for subject processing the multispectral satellite images based on fuzzy clustering and preliminary non-linear filtering has presented. Three fuzzy clustering algorithms, namely Fuzzy C-means, Gustafson-Kessel, and Gath-Geva have been utilized. The experimental results obtained using these algorithms with and without preliminary nonlinear filtering to segment multispectral Landsat images have testify that segmentation based on fuzzy clustering provides good-looking discrimination of different land cover types. Implementations of Fuzzy C-means, Gustafson-Kessel, and Gath-Geva algorithms have got linear computational complexity depending on innitial cluster amount and image size for single iteration step. They assume internal parallel implementation. The preliminary processing of source channels with nonlinear filter provides more clear cluster discrimination and has as a consequence more clear segment outlining.

An Experiment in Distributed Visual Attention

P. Bachiller, P. Bustos, J. M. Cañas and R. Royo

Attention mechanisms of biological vision have been applied to machine vision for several applications, like visual search and object detection. Most of the proposed models are centred on a unique way of attention, mainly stimulus-driven or bottom-up attention. We propose a visual attention system that integrates several attentional behaviours. To get a real-time implementa-tion, we have designed a distributed software architecture that exhibits an effi-cient and flexible structure. We describe some implementation details and real experiments performed in a mobile robot endowed with a stereo vision head.

Multi-Robot Task Allocation with Tightly Coordinated Tasks and Deadlines using Market-Based Methods

Robert Gaimari, Guido Zarrella and Bradley Goodman

The domain of Collaborative Time Sensitive Targeting requires agents to assess and prioritize tasks, dynamically form heterogeneous teams of agents to perform the tightly coordinated tasks, and to complete them within time deadlines. In this paper, we describe extensions to market-based, multi-robot task allocation to allow for these requirements.

A Bio-inspired Multi-Robot Coordination Approach

Yan Meng, Ọlọrundamilọla Kazeem and Jing Gan

In this paper, a bio-inspired coordination approach is proposed for a distributed multi-robot system. This approach combines the feedback mechanism from environment of Ant Colony Optimization (ACO) and the adaptive interplay among agents of Particle Swarm Optimization (PSO) to create a dynamic optimization system, and it is well-suited for a large scale distributed multi-agent system under dynamic environments. A virtual pheromone mechanism is proposed as the message passing coordination scheme among the robots. Furthermore, a pheromone-edge pair propagation funneling method is developed to reduce the communication overhead among robots. The simula-tion results concretely demonstrate the robustness, scalability, and individual simplicity of the proposed control architecture in a swarm robot system with real-world constraints.

A Hybrid Dynamic Task Allocation Approach for a Heterogeneous Multi-Robot System

Yan Meng and Kashyap Shah

In this paper, we propose a hybrid task scheduling algorithm for a multi-robot system in a dynamic unknown environment, where each robot makes its own decision through communicating with others as well as checks a global task status queue to improve the coordination efficiency. This algorithm avoids unnecessary communi-cation by broadcasting global information which is in everybody’s interest and meanwhile limits specific information which is in interest of some specific robots only. The proposed algorithm takes advantage of centralized approaches to improve the overall efficiency and distributed approaches to reduce the communication over-head, which automatically leads to reduction of power consumption and time con-sumption. In addition, by tracking the communication signal that it has sent and expected to receive, each robot would dynamically allocate the task to robots which are capable and most available. This feature makes the system robust against communication failures and robot failures.

A Complexity Theory Approach to Evolvable Production Systems

Regina Frei, José Barata and Giovanna Di Marzo Serugendo

Evolvable Production Systems differ from Reconfigurable and Holonic Manufacturing Systems by providing a concrete solution, implying truly process-specific modularity at fine granularity with local intelligence and a dis-tributed control solution based on the Multi-Agent paradigm. Understanding the dynamics of such complex production systems and conceiving new system capabilities is not feasible with traditional engineering. For creating the manu-facturing systems of the future, engineers need to dare a leap in their ways of thinking. Complexity Theory and Artificial Intelligence can be a valuable source of inspiration for manufacturing engineers. This article illustrates how ideas from these scientific areas fit the problems and open questions of manu-facturing. Some concepts, as Self-Organization and Emergence, need adapta-tion to be applicable in production systems; others simply require the right per-spective. Finally, a vision of future EPS is outlined.

Cooperative Collision Avoidance between Multiple Robots based on Bernstein-Bézier Curves

Igor Skrjanc and Gregor Klancar

In this paper a new cooperative collision-avoidance method for multiple nonholonomic robots based on Bernstein- Bezier curves is presented. The reference path of each robot from the start pose to the goal pose, is obtained by minimizing the penalty function, which takes into account the sum of all the paths subjected to the distances between the robots, which should be bigger than the minimal distance defined as the safety distance. When the reference paths are defined the model predictive trajectory tracking is used to define the control. A prediction model derived from linearized tracking-error dynamics is used to predict future system behavior. A control law is derived from a quadratic cost function consisting of the system tracking error and the control effort. The results of the simulation and some future work ideas are discussed.

Towards a Generic Anticipatory Agent Architecture for Mobile Robots

Noury Bouraqadi and Serge Stinckwich

An anticipatory agent is a hybrid agent which is able to predict changes of itself and its environment. Such agents prove interesting in embedded systems such mobile robots. Indeed, they combine a reactive fast layer with a cognitive layer capable to perform corrective actions to avoid undesired situations before they occur actually. We present in this paper a generic architecture that we plan to use as a guideline for developing anticipatory agents embedded into robots for search & rescue missions. Our approach relies on software components in order to explicit the anticipatory mechanisms.

Ultrasound Sensor Array for Robust Location

José N. Vieira, Sérgio I. Lopes, Carlos A. C. Bastos and Pedro N. Fonseca

In this paper we present a system for localizing a team of soccer robots using ultrasound. The proposed system uses chirp signals to obtain a better signal to noise ratio with good time resolution and improved interference immunity. An array of four ultrasonic sensors is used to obtain spatial diversity and reduce the localization error. An efficient DSP algorithm for base-band conversion and decimation of the received pulses is also presented. The proposed system based on the TI DSP 2812 is very efficient and allows the localization of the robots up to 8 meters with an angular error with a maximum standard deviation of 2º.

BESA-ME: Framework for Robotic MultiAgent System Design

David M. Flórez, Guillermo A. Rodríguez, Juan M. Ortiz and Enrique González

BESA-ME is a software middleware designed to make easier and to improve the construction of robotic control systems based on multi-agent techniques. BESA is a behavior-oriented, event-driven and social-based general purpose architecture designed to build concurrent applications using the multi-agent paradigm. The BESA abstract model incorporates the concept of behavior and the management of asynchronous events, which are very useful in the construction of robotic systems, thus it allows to design robot control architectures in a natural and direct fashion. BESA-ME, micro-edition, is the adapted BESA model that is well suited to be implemented over microcontrollers in embedded systems. Initially, it has been developed for the PIC18F chips family, and then adapted for dsPIC60F chips family, both under the real time operating system FreeRTOS ™.

To Add with Caution —Decreasing a Swarm Robotics’ Efficiency by Imprudently Enhancing the Robots’ Capabilities

Yaniv Altshuler, Israel A. Wagner and Alfred M. Bruckstein

This work discusses the common opinion among robotics systems’ designer, assuming that for a given assignment and robotics system, enhancing the robots by increasing their physical capabilities, may only result in an improvement in the overall performance of the system (albeit small). Therefore, a designer may rely on existing designs prepared in the past, and by continuously adding resources to the robots, finally achieve the overall system’s performance he is interested in. As it can be shown, this assumption is wrong, as it may not only lead to a zero increase in the performance, but even to a new system, comprising far more advance (and expensive) robots, which achieve much worse results than the original system. The work presents an example concerning the problem of multi-robots exploration of a graph, in which adding communication features to the robots causes the entire system’s performance to drop significantly.

An Elementary Communication Framework for Open Co-operative RoboCup Soccer Teams

Luís Mota and Luís Paulo Reis

One of the present day challenges in RoboCup is the development of Open Co-operative teams, where different research labs join efforts to build a common team. In RoboCup Middle-size League, some teams were confronted with a pairwise cooperation scenario in order to qualify to the competition. Such teams bring together robots with heterogeneous hardware, architectures and control software, which hinders straightforward co-operation. The robots in these teams might co-operate through a-priori strategic knowledge and structured communication during the game. This paper presents the kernel of a communication framework, defining a robotic soccer vocabulary, as well as rules to manage communication.

Robustness Against Deception in Unmanned Vehicle Decision Making

William M. McEneaney and Rajdeep Singh

We are motivated by the tasking problem for UAVs in an adversarial environment. In particular, we consider the problem where, in addition to purely random noise in the observation process, the opponent may be applying deception as a means to cause us to make poor tasking choices. The standard approach would be to apply the feedback-optimal controls for the fully-observed game, to a maximum-likelihood state estimate. We find that such an approach is highly suboptimal. A second approach is through a concept taken from risk-sensitive control. For the third approach, we formulate and solve the problem directly as a partially-observed stochastic game. A chief problem with such a formulation is that the information state for the player with imperfect information is a function over the space of probability distributions (a function over a simplex), and so infinite-dimensional. However, under certain conditions, we find that the information state is finite-dimensional. Computational tractability is greatly enhanced. A simple example is considered, and the three approaches are compared. We find that the third approach is yields the best results (for such a case), although computational complexity may lead to use of the second approach on larger problems.