decentralized estimation and control for multisensor systems

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  • DecentralizedEstimation and Controlfor Multisensor Systems

  • 2DecentralizedEstimation and Controlfor MultisensorSystems

    Arthur G.O..Mutambara

    CRC PressBoca Raton Boston London New York Washington, D.C.

  • 1998 by CRC Press LLC

    Trademark Notice: Product or corporate names may be trademarks or registered trademarks,and are used only for identification and explanation, without intent to infringe.

    No claim to original U.S. Government worksInternational Standard Book Number 0-8493-1865-3Library of Congress Card Number 97-51553Printed in the United States of America 1 2 3 4 5 6 7 8 9 0Printed on acid-free paper


    This book is concerned with the problem of developing scalable, decen-tralized estimation and control algorithms for both linear and non linearmultisensor systems. Such algorithms have extensive applications in mod-ular robotics and complex or large scale systems. Most existing algorithmsemploy some form of hierarchical or centralized structure for data gatheringand processing. In contrast, in a fully decentralized system, all informa-tion is processed locally. A decentralized data fusion system consists ofa network of sensor nodes, each with its own processing facility, whichtogether do not require any central processing or central communicationfacility. Only node-to-node communication and local system knowledge ispermitted.

    Algorithms for decentralized data fusion systems based on the linear In-formation filter have previously been developed. These algorithms obtaindecentrally exactly the same results as those obtained in a conventionalcentralized data fusion system. However, these algorithms are limited inrequiring linear system and observation models, a fully connected sensornetwork topology, and a complete global system model to be maintainedby each individual node in the network. These limitations mean that ex-isting decentralized data fusion algorithms have limited scalability and arewasteful of communication and computation resources.

    This book aims to remove current limitations in decentralized data fusionalgorithms and further to extend the decentralized estimation principle toproblems involving local control and actuation. The linear Information fil-ter is first generalized to the problem of estimation for nonlinear systemsby deriving the extended Information filter. A decentralized form of thealgorithm is then developed. The problem of fully connected topologies issolved by using generalized model distribution where the nodal system in-volves only locally relevant states. Computational requirements are reducedby using smaller local model sizes. Internodal communication is model de-fined such that only nodes that need to communicate are connected. Whennodes communicate they exchange only relevant information. In this way,


    Library of Congress Cataloging-in-Publication Data

    Mutambara, Arthur G.O.Decentralized estimation and control for multisensorsystems /

    [Arthur G.O. Mutambara].p. cm.

    Includes bibliographical references and index.ISBN 0-8493-1865-3 (alk. paper)1. Multisensor data fusion. 2. Automatic control. 3. Robots-

    -Control systems. I. Title.TJ211.35.M88 1998629.8 -dc21

    This book contains information obtained from authentic and highly regarded sources. Reprintedmaterial is quoted with permission, and sources are indicated. A wide variety of references are listed.Reasonable efforts have been made to publish reliable data and information, but the author and thepublisher cannot assume responsibility for the validity of all materials or for the consequences oftheir use.

    Neither this book nor any part may be reproduced or transmitted in any form or by any means,electronic or mechanical, including photocopying, microfilming, and recording, or by any informationstorage or retrieval system, without prior permission in writing from the publisher.

    The consent of CRC Press LLC does not extend to copying for general distribution, for promotion,for creating new works, or for resale. Specific permission must be obtained in writing from CRCPress LLC for such copying.

    Direct all inquiries to CRC Press LLC, 2000 Corporate Blvd., N.W., Boca Raton, Florida 33431.

  • communication is minimized both in terms of the number of communicationlinks and size of message. The scalable network does not require propaga-tion of information between unconnected nodes. Estimation algorithms forsystems with different models at each node are developed.

    The decentralized estimation algorithms are then applied to the problemof decentralized control. The control algorithms are explicitly described interms of information. Optimal control is obtained locally using reducedorder models with minimized communication requirements, in a scalablenetwork of control nodes. A modular wheeled mobile robot is used todemonstrate the theory developed. This is a vehicle system with nonlinearkinematics and distributed means of acquiring information.

    Although a specific modular robot is used to illustrate the usefulnessof the algorithms, their application can be extended to many robotic sys-tems and large scale systems. Specifically, the modular design philosophy,decentralized estimation and scalable control can be applied to the MarsSojourner Rover with dramatic improvement of the Rover's performance,competence, reliability and survivability. The principles of decentralizedmultisensor fusion can also be considered for humanoid robots such as theMIT Humanoid Robot (Cog). Furthermore, the proposed decentralizationparadigm is widely useful in complex and large scale systems such as airtraffic control, process control of large plants, the Mir Space Station andspace shuttles such as Columbia.

    The Author

    Dr. Arthur G.O. Mutambara is an Assistant Professor of Robotics andMechatronics in the Mechanical Engineering Department at the joint En-gineering College of Florida Agricultural and Mechanical University andFlorida State University in Tallahassee, Florida (U.S.A.). He has beena Visiting Research Fellow at the Massachusetts Institute of Technology(MIT) in the Astronautics and Aeronautics Department (1995), at theCalifornia Institute of Technology (CaITech) (1996) and at the NationalAeronautics and Space Administration (NASA), Jet Propulsion .. Labora-tory, in California (1994). In 1997 he was a Visiting Research Scientist atthe NASA Lewis Research Center in Cleveland, Ohio. He has served onboth the Robotics Review Panel and the Dynamic Systems and ControlsPanel for the U.S.A. National Science Foundation (NSF).

    Professor Mutambara received the Doctor of Philosophy degree in Robot-ics from Merton College, Oxford University (U.K.) in March 1995, wherehe worked with the Robotics Research Group. He went to Oxford as aRhodes Scholar and also earned a Master of Science in Computation fromthe Oxford University Computing Laboratory in October 1992, where heworked with the Programming Research Group. Prior to this, he had re-ceived a Bachelor of Science with Honors in Electrical Engineering from theUniversity of Zimbabwe in 199L

    Professor Mutambara's main research interests include multisensor fu-sion, decentralized estimation, decentralized control, mechatronics and mod-ular robotics. He teaches graduate and undergraduate courses in robotics,mechatronics, control systems, estimation theory, dynamic systems and vi-brations. He is a Membet of the Institute of Electrical and ElectronicEngineering (IEEE), the Institute of Electrical Engineering (lEE) and theBritish Computer Society (BCS).

  • Acknowledgments

    The research material covered in this book is an extension of the work I didfor my Doctor of Philosophy degree at Oxford University where I workedwith the Robotics Research Group. It is with great pleasure that I acknowl-edge the consistent and thorough supervision provided by Professor HughDurrant-Whyte of the Robotics Research Group, who is .. now Professor ofMechatronics Engineering at the University of Sydney in Australia. Hisresourcefulness and amazing subject expertise were a constant source ofinspiration. Professor Mike Brady, Head of the Robotics Research Groupat Oxford, was always accessible and supportive. My fellow graduate stu-dents in the Robotics Research Group provided the requisite team spiritand enthusiasm.

    After finishing my Doctorate at Oxford University in March 1995, I tookup a Visiting Research Fellowship at the Massachusetts Institute of Tech-nology (MIT) in the Astronautics and Aeronautics Department where Icarried out additional research with the Space Engineering Research Cen-ter (SERC). I would like to thank Professor Edward Crawley for invitingme to MIT and for his insightful comments. I would also like to thankProfessor Rodney Brooks of the Artificial Intelligence (AI) laboratory atMIT for facilitating visits to the AI laboratory and providing informationabout the MIT Humanoid Robot (Cog). Further work on the book wascarried out at the National Aeronautics and Space Administration (NASA)Lewis Research Center in Cleveland Ohio, where I was a Summer FacultyResearch Fellow in 1997. I would like to thank Dr. Jonathan Litt of NASALewis for affording me that opportunity.

    Quite a number of experts reviewed and appraised the material coveredin this book. In particular, I would like to thank the following for theirdetailed remarks and suggestions: Professor Yaakov Bar-Shalom of theElectrical and Systems Engineering at University of Connecticut, ProfessorPeter Fleming. who is Chairman of the Department of Automatic ControlEngineering at the University of Sheffield (U.K.), Dr. Ron Daniel of theRobotics Research Group at the University of Oxford, Dr. Jeff Uhlmann

  • and Dr. Simon Julier, who are both at the Naval Research Laboratory(NRL) in Washington D.C. I would also like to thank all my colleagu