New Developments in Robotics, Automation and Control New Developments in Robotics, Automation and Control Edited by Aleksandar Lazinica In-Tech IV Published by In-Tech Abstracting and non-profit use of the material is permitted with credit to the source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. Publisher assumes no responsibility liability for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained inside. After this work has been published by the In-Tech, authors have the right to republish it, in whole or part, in any publication of which they are an author or editor, and the make other personal use of the work. © 2008 In-tech http://intechweb.org/ Additional copies can be obtained from: publication@ars-journal.com First published November 2008 Printed in Croatia A catalogue record for this book is available from the University Library Rijeka under no. 120104021 New Developments in Robotics, Automation and Control, Edited by Aleksandar Lazinica p. cm. ISBN 978-953-7619-20-6 1. Robotics. 2. Automation V Preface This book represents the contributions of the top researchers in the field of robotics, automation and control and will serve as a valuable tool for professionals in these interdis- ciplinary fields. The book consists of 25 chapters introducing both basic research and advanced develop- ments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control. This book is certainly a small sample of the research activity going on around the globe as you read it, but it surely covers a good deal of what has been done in the field recently, and as such it works as a valuable source for researchers interested in the involved subjects. Special thanks to all authors, which have invested a great deal of time to write such inter- esting and high quality chapters. Editor Aleksandar Lazinica VII Contents Preface V 1. The Area Coverage Problem for Dynamic Sensor Networks 001 Simone Gabriele and Paolo Di Giamberardino 2. Multichannel Speech Enhancement 027 Lino García and Soledad Torres-Guijarro 3. Multiple Regressive Model Adaptive Control 059 Emil Garipov, Teodor Stoilkov and Ivan Kalaykov 4. Block-synchronous harmonic control for scalable trajectory planning 085 Bernard Girau, Amine Boumaza, Bruno Scherrer and Cesar Torres-Huitzil 5. Velocity Observer for Mechanical Systems 111 Ricardo Guerra, Claudiu Iurian and Leonardo Acho 6. Evolution of Neuro-Controllers for Trajectory Planning Applied to a Bipedal Walking Robot with a Tail 121 Álvaro Gutiérrez, Fernando J. Berenguer and Félix Monasterio-Huelin 7. Robotic Proximity Queries Library for Online Motion Planning Applications 143 Xavier Giralt, Albert Hernansanz, Alberto Rodriguez and Josep Amat 8. Takagi-Sugeno Fuzzy Observer for a Switching Bioprocess: Sector Nonlinearity Approach 155 Enrique J. Herrera-López, Bernardino Castillo-Toledo, Jesús Ramírez-Córdova and Eugénio C. Ferreira 9. An Intelligent Marshalling Plan Using a New Reinforcement Learning System for Container Yard Terminals 181 Yoichi Hirashima 10. Chaotic Neural Network with Time Delay Term for Sequential Patterns 195 Kazuki Hirozawa and Yuko Osana 11. PDE based approach for segmentation of oriented patterns 207 Aymeric Histace, Michel Ménard and Christine Cavaro-Ménard 12. The robot voice-control system with interactive learning 219 Miroslav Holada and Martin Pelc VIII 13. Intelligent Detection of Bad Credit Card Accounts 229 Yo-Ping Huang, Frode Eika Sandnes, Tsun-Wei Chang and Chun-Chieh Lu 14. Improved Chaotic Associative Memory for Successive Learning 247 Takahiro Ikeya and Yuko Osana 15. Kohonen Feature Map Associative Memory with Refractoriness based on Area Representation 259 Tomohisa Imabayashi and Yuko Osana 16. Incremental Motion Planning With Las Vegas Algorithms 273 Jouandeau Nicolas, Touati Youcef and Ali Cherif Arab 17. Hierarchical Fuzzy Rule-Base System for MultiAgent Route Choice 285 Habib M. Kammoun, Ilhem Kallel and Adel M. Alimi 18. The Artificial Neural Networks applied to servo control systems 303 Yuan Kang , Yi-Wei Chen, Ming-Huei Chu and Der-Ming Chry 19. Linear Programming in Database 339 Akira Kawaguchi and Andrew Nagel 20. Searching Model Structures Based on Marginal Model Structures 355 Sung-Ho Kim and Sangjin Lee 21. Active Vibration Control of a Smart Beam by Using a Spatial Approach 377 Ömer Faruk Kircali, Yavuz Yaman,Volkan Nalbantoğlu and Melin Şahin 22. Time-scaling in the control of mechatronic systems 411 Bálint Kiss and Emese Szádeczky-Kard 23. Heap Models, Composition and Control 427 Jan Komenda, Sébastien Lahaye* & Jean-Louis Boimond 24. Batch Deterministic and Stochastic Petri Nets and Transformation Analysis Methods 449 Labadi Karim, Amodeo Lionel and Haoxun Chen 25. Automatic Estimation of Parameters of Complex Fuzzy Control Systems 475 Yulia Ledeneva, René García Hernández and Alexander Gelbukh 1 The Area Coverage Problem for Dynamic Sensor Networks Simone Gabriele, Paolo Di Giamberardino Università degli Studi di Roma ”La Sapienza” Dipartimento di Informatica e Sistemistica ”Antonio Ruberti” Italy 1. Introduction In this section a brief description of area coverage and connectivity maintenance for sensor networks is given together with their collocation in the scientific literature. Particular attention is given to dynamic sensor networks, such as sensor networks in witch sensing nodes moves continuously, under the assumption, reasonable in many applications, that synchronous or asynchronous discrete time measures are acceptable instead of continuous ones. 1.1 Area Coverage Environmental monitoring of lands, seas or cities, cleaning of parks, squares or lakes, mine clearance and critical structures surveillance are only a few of the many applications that are connected with the concept of area coverage. Area coverage is always referred to a set, named set of interest, and to an action: then, covering means acting on all the physical locations of the set of interest. Within the several actions that can be considered, such as manipulating, cleaning, watering and so on, sensing is certainly one of the most considered in literature. Recent technological advances in wireless networking and miniaturizing of electronic computers, have suggested to face the problem of taking measures on large, hazardous and dynamic environments using a large number of smart sensors, able to do simple elaborations an perform data exchange over a communication network. This kind of distributed sensors systems have been named, by the scientific and engineering community, sensor networks. Coverage represents a significant measure of the quality of service provided by a sensor network. Considering static sensors, the coverage problem has been addressed in terms of optimal usage of a given set of sensors, randomly deployed, in order to assure full coverage and minimizing energy consumption (Cardei and Wu, 2006, Zhang and Hou, 2005, Stojmenovic, 2005), or in terms of optimal sensors deployment on a given area, such as optimizing sensors locations, as in (Li et al., 2003, Meguerdichian et al., 2001, Chakrabarty et al., 2002, Isler et al., 2004, Zhou et al., 2004). The introduction of mobile sensors allows to develop networks in which sensors, starting from an initial random deployment condition, evaluate and move trough optimal locations. New Developments in Robotics, Automation and Control 2 In (Li and Cassandras, 2005) coverage maximization using sensors with limited range, while minimizing communications cost, is formulated as an optimization problem. A gradient algorithm is used to drive sensors from initial positions to suboptimal locations. In (Howard, 2002) an incremental deployment algorithm is presented. Nodes are deployed one-at- time into an unknown complex environment, with each node making use of information gathered by previously deployed nodes. The algorithm is designed to maximize network coverage while ensuring line-of-sight between nodes. A stable feedback control law, in both continuous and discrete time, to drive sensors to so- called centroidal Voronoy configurations, that are critical points of the sensors locations optimization problem, is presented in (Cortes et al., 2004). Other interesting works on self deploying or self configuring sensor networks are (Cheng and Tsai, 2003, Sameera and Gaurav S., 2004, Tsai et al., 2004) The natural evolution of these kind of approaches moves in the direction of giving a greater motion capabilities to the network. And once the sensors can move autonomously in the environment, the measurements can be performed also during the motion (dynamic coverage). Then, under the assumption, reasonable in many applications, that synchronous or asynchronous discrete time measures are acceptable instead of continuous ones, the number of sensors can be strongly reduced. Moreover, faults or critical situations can be faced and solved more efficiently, simply changing the paths of the working moving sensors. Clearly, coordinated motion of such dynamic sensor network, imposes additional requirements, such as avoiding collisions or preserving communication links between sensors. In order to better motivate why and when a mobile sensor network can be a more successful choice than a static one, some considerations are reported. So, given an area to be measured by a sensor network, and the measure range of each sensor (sensors are here supposed homogeneous, otherwise the same considerations should be repeated for all the homogeneous subnets), the number of sensors needed for a static network must satisfy (1) When a dynamic network is considered, the area covered by sensors is a time function and, clearly, it not decreases as time passes. A simplified discrete time model of the evolution of the area still uncovered, at (discrete) time , by a dynamic sensor network moving with the strategy proposed in this chapter, can be given by the following differences equation (2) where [...]... described by: New Developments in Robotics, Automation and Control 18 (30) and (31) As done in 3.1, is possible to define generalized input, state and output sequences of the whole system: These sequences are related by: (32) And (33) The Area Coverage Problem for Dynamic Sensor Networks 19 4.2 Coverage Problem Formulation Using the coverage model defined in 3.2 and the communication model in 3.3, it... coordinates, is not too small In addition, each mobile platform representing the nodes of the net has been considered equipped with different sets of sensors, so introducing a non homogeneity in the sensor network A general formulation of the field coverage problem as been introduced in New Developments in Robotics, Automation and Control 24 terms of optimal control techniques All the constraints introduced... 81(7):1302–1314 Gabriele, S and Di Giamberardino, P (2007c) Dynamic sensor networks an approach to optimal dynamic field coverage In ICINCO 2007, Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics, Intelligent Control Systems and Optimization Gabriele, S and Di Giamberardino, P (2008) Mobile sensors networks under communication constraints WSEAS Transactions... A., and Murray, R M (2007) Consensus and cooperation in networked multi-agent systems Proceedings of the IEEE, 95:215–233 Olfati-Saber, R and Murray, R (2002) Distributed structural stabilization and tracking for formations of dynamic multi-agents In Decision and Control, 2002, Proceedings of the 41st IEEE Conference on, volume 1, pages 209–215vol.1 New Developments in Robotics, Automation and Control. .. possible to constrain sensors to move inside a box subset of If needed is possible to set the staring and/ or the final state (positions and/ or speeds): A particular case is the periodic trajectories constrain, useful in tasks in which measures have to be repeated continuously: Is also necessary to avoid collisions between sensors at every time New Developments in Robotics, Automation and Control 14 for... Optimal path planing based on visibility Journal of Optimization Theory and Applications, 117:157–181 Zavlanos, M.M Pappas, G (2005) Controlling connectivity of dynamic graphs In Decision and Control, 2005 and 2005 European Control Conference CDC-ECC ’05 44th IEEE Conference on Zhang, H and Hou, J C (2005) Maintaining sensing coverage and connectivity in large sensor networks Ad Hoc and Sensor Wireless... configuration and generalized input as: At the same manner the generalized dynamic of the whole network can be written as: 2.2 Coverage Let indicate with interval the evolution of sensor configuration during a given a time It is possible to define the subset of by during as: (5) 8 New Developments in Robotics, Automation and Control Considering generalized configuration measure, respect to magnitude during... Hussein, I I., Stipanovic, D M., and Wang, Y (2007) Reliable coverage control using heterogeneous vehicles In Decision and Control, 2007 46th IEEE Conference on, pages 6142–6147 Isler, V., Kannan, S., and Daniilidis, K (2004) Sampling based sensor-network deployment In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems IROS Kim, Y and Mesbahi, M (2005) On maximizing the... the minimum number of sensors (with given and ) required to cover a given fraction of the area of interest according to a given measurement rate In fact, it is possible to write the relation between the maximum rate at witch the network can cover the number of moving sensors as fraction of and the New Developments in Robotics, Automation and Control 4 (4) Such a relationship between and is depicted in. .. Mathematics and Artificial Intelligence, 31:113–126 Cortes, J., Martinez, S., Karatas, T., and Bullo, F (2004) Coverage control for mobile sensing networks IEEE Transactions on Robotics and Automation, 20:243–255 Gabriele, S and Di Giamberardino, P (2007a) Communication constraints for mobile sensor networks In Proceedings of the 11th WSEAS International Conference on Systems Gabriele, S and Di Giamberardino, . New Developments in Robotics, Automation and Control New Developments in Robotics, Automation and Control. researchers in the field of robotics, automation and control and will serve as a valuable tool for professionals in these interdis- ciplinary fields. The book consists of 25 chapters introducing. locations. New Developments in Robotics, Automation and Control 2 In (Li and Cassandras, 2005) coverage maximization using sensors with limited range, while minimizing communications cost,