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Nourbakhsh, 2004 Roland Siegwart and Illah R. Nourbakhsh A Bradford Book The MIT Press Cambridge, Massachusetts London, England ? 2004 Massachusetts Institute of Technology All rights reserved. No part of this book may be reproduced in any form by any electronic or mechan- ical means (including photocopying, recording, or information storage and retrieval) without permis- sion in writing from the publisher. This book was set in Times Roman by the authors using Adobe FrameMaker 7.0. Printed and bound in the United States of America. Library of Congress Cataloging-in-Publication Data Siegwart, Roland. Introduction to autonomous mobile robots / Roland Siegwart and Illah Nourbakhsh. p. cm. — (Intelligent robotics and autonomous agents) “A Bradford book.” Includes bibliographical references and index. ISBN 0-262-19502-X (hc : alk. paper) 1. Mobile robots. 2. Autonomous robots. I. Nourbakhsh, Illah Reza, 1970– . II. Title. III. Series. TJ211.415.S54 2004 629.8′92—dc22 2003059349 To Luzia and my children Janina, Malin and Yanik who give me their support and freedom to grow every day — RS To my parents Susi and Yvo who opened my eyes — RS To Marti who is my love and my inspiration — IRN To my parents Fatemeh and Mahmoud who let me disassemble and investigate everything in our home — IRN Slides and exercises that go with this book are available on: http://www.mobilerobots.org Contents Acknowledgments xi Preface xiii 1 Introduction 1 1.1 Introduction 1 1.2 An Overview of the Book 10 2 Locomotion 13 2.1 Introduction 13 2.1.1 Key issues for locomotion 16 2.2 Legged Mobile Robots 17 2.2.1 Leg configurations and stability 18 2.2.2 Examples of legged robot locomotion 21 2.3 Wheeled Mobile Robots 30 2.3.1 Wheeled locomotion: the design space 31 2.3.2 Wheeled locomotion: case studies 38 3 Mobile Robot Kinematics 47 3.1 Introduction 47 3.2 Kinematic Models and Constraints 48 3.2.1 Representing robot position 48 3.2.2 Forward kinematic models 51 3.2.3 Wheel kinematic constraints 53 3.2.4 Robot kinematic constraints 61 3.2.5 Examples: robot kinematic models and constraints 63 3.3 Mobile Robot Maneuverability 67 3.3.1 Degree of mobility 67 3.3.2 Degree of steerability 71 3.3.3 Robot maneuverability 72 viii Contents 3.4 Mobile Robot Workspace 74 3.4.1 Degrees of freedom 74 3.4.2 Holonomic robots 75 3.4.3 Path and trajectory considerations 77 3.5 Beyond Basic Kinematics 80 3.6 Motion Control (Kinematic Control) 81 3.6.1 Open loop control (trajectory-following) 81 3.6.2 Feedback control 82 4 Perception 89 4.1 Sensors for Mobile Robots 89 4.1.1 Sensor classification 89 4.1.2 Characterizing sensor performance 92 4.1.3 Wheel/motor sensors 97 4.1.4 Heading sensors 98 4.1.5 Ground-based beacons 101 4.1.6 Active ranging 104 4.1.7 Motion/speed sensors 115 4.1.8 Vision-based sensors 117 4.2 Representing Uncertainty 145 4.2.1 Statistical representation 145 4.2.2 Error propagation: combining uncertain measurements 149 4.3 Feature Extraction 151 4.3.1 Feature extraction based on range data (laser, ultrasonic, vision-based ranging) 154 4.3.2 Visual appearance based feature extraction 163 5 Mobile Robot Localization 181 5.1 Introduction 181 5.2 The Challenge of Localization: Noise and Aliasing 182 5.2.1 Sensor noise 183 5.2.2 Sensor aliasing 184 5.2.3 Effector noise 185 5.2.4 An error model for odometric position estimation 186 5.3 To Localize or Not to Localize: Localization-Based Navigation versus Programmed Solutions 191 5.4 Belief Representation 194 5.4.1 Single-hypothesis belief 194 5.4.2 Multiple-hypothesis belief 196 Contents ix 5.5 Map Representation 200 5.5.1 Continuous representations 200 5.5.2 Decomposition strategies 203 5.5.3 State of the art: current challenges in map representation 210 5.6 Probabilistic Map-Based Localization 212 5.6.1 Introduction 212 5.6.2 Markov localization 214 5.6.3 Kalman filter localization 227 5.7 Other Examples of Localization Systems 244 5.7.1 Landmark-based navigation 245 5.7.2 Globally unique localization 246 5.7.3 Positioning beacon systems 248 5.7.4 Route-based localization 249 5.8 Autonomous Map Building 250 5.8.1 The stochastic map technique 250 5.8.2 Other mapping techniques 253 6 Planning and Navigation 257 6.1 Introduction 257 6.2 Competences for Navigation: Planning and Reacting 258 6.2.1 Path planning 259 6.2.2 Obstacle avoidance 272 6.3 Navigation Architectures 291 6.3.1 Modularity for code reuse and sharing 291 6.3.2 Control localization 291 6.3.3 Techniques for decomposition 292 6.3.4 Case studies: tiered robot architectures 298 Bibliography 305 Books 305 Papers 306 Referenced Webpages 314 Interesting Internet Links to Mobile Robots 314 Index 317 Acknowledgments This book is the result of inspirations and contributions from many researchers and students at the Swiss Federal Institute of Technology Lausanne (EPFL), Carnegie Mellon Univer- sity’s Robotics Institute, Pittsburgh (CMU), and many others around the globe. We would like to thank all the researchers in mobile robotics that make this field so rich and stimulating by sharing their goals and visions with the community. It is their work that enables us to collect the material for this book. The most valuable and direct support and contribution for this book came from our past and current collaborators at EPFL and CMU. We would like to thank: Kai Arras for his con- tribution to uncertainty representation, feature extraction and Kalman filter localization; Matt Mason for his input on kinematics; Nicola Tomatis and Remy Blank for their support and assistance for the section on vision-based sensing; Al Rizzi for his guidance on feed- back control; Roland Philippsen and Jan Persson for their contribution to obstacle avoid- ance; Gilles Caprari and Yves Piguet for their input and suggestions on motion control; Agostino Martinelli for his careful checking of some of the equations and Marco Lauria for offering his talent for some of the figures. Thanks also to Marti Louw for her efforts on the cover design. This book was also inspired by other courses, especially by the lecture notes on mobile robotics at the Swiss Federal Institute of Technology, Zurich (ETHZ). Sincere thank goes to Gerhard Schweitzer, Martin Adams and Sjur Vestli. At the Robotics Institute special thanks go to Emily Hamner and Jean Harpley for collecting and organizing photo publica- tion permissions. The material for this book has been used for lectures at EFPL and CMU since 1997. Thanks go to all the many hundreds of students that followed the lecture and contributed thought their corrections and comments. It has been a pleasure to work with MIT Press, publisher of this book. Thanks to Ronald C. Arkin and the editorial board of the Intelligent Robotics and Autonomous Agents series for their careful and valuable review and to Robert Prior, Katherine Almeida, Sharon Deacon Warne, and Valerie Geary from MIT Press for their help in editing and finalizing the book. Special thanks also to Marie-Jo Pellaud at EPFL for carefully correcting the text files and to our colleagues at the Swiss Federal Institute of Technology Lausanne and Carnegie Mellon University. Preface Mobile robotics is a young field. Its roots include many engineering and science disci- plines, from mechanical, electrical and electronics engineering to computer, cognitive and social sciences. Each of these parent fields has its share of introductory textbooks that excite and inform prospective students, preparing them for future advanced coursework and research. Our objective in writing this textbook is to provide mobile robotics with such a preparatory guide. This book presents an introduction to the fundamentals of mobile robotics, spanning the mechanical, motor, sensory, perceptual and cognitive layers that comprise our field of study. A collection of workshop proceedings and journal publications could present the new student with a snapshot of the state of the art in all aspects of mobile robotics. But here we aim to present a foundation — a formal introduction to the field. The formalism and analysis herein will prove useful even as the frontier of the state of the art advances due to the rapid progress in all of mobile robotics' sub-disciplines. We hope that this book will empower both the undergraduate and graduate robotics stu- dent with the background knowledge and analytical tools they will need to evaluate and even critique mobile robot proposals and artifacts throughout their career. This textbook is suitable as a whole for introductory mobile robotics coursework at both the undergraduate and graduate level. Individual chapters such as those on Perception or Kinematics can be useful as overviews in more focused courses on specific sub-fields of robotics. The origins of the this book bridge the Atlantic Ocean. The authors have taught courses on Mobile Robotics at the undergraduate and graduate level at Stanford University, ETH Zurich, Carnegie Mellon University and EPFL (Lausanne). Their combined set of curricu- lum details and lecture notes formed the earliest versions of this text. We have combined our individual notes, provided overall structure and then test-taught using this textbook for two additional years before settling on the current, published text. For an overview of the organization of the book and summaries of individual chapters, refer to Section 1.2. Finally, for the teacher and the student: we hope that this textbook proves to be a fruitful launching point for many careers in mobile robotics. That would be the ultimate reward. 1 Introduction 1.1 Introduction Robotics has achieved its greatest success to date in the world of industrial manufacturing. Robot arms, or manipulators, comprise a 2 billion dollar industry. Bolted at its shoulder to a specific position in the assembly line, the robot arm can move with great speed and accu- racy to perform repetitive tasks such as spot welding and painting (figure 1.1). In the elec- tronics industry, manipulators place surface-mounted components with superhuman precision, making the portable telephone and laptop computer possible. Yet, for all of their successes, these commercial robots suffer from a fundamental dis- advantage: lack of mobility. A fixed manipulator has a limited range of motion that depends ? KUKA Inc. ? SIG Demaurex SA Figure 1.1 Picture of auto assembly plant-spot welding robot of KUKA and a parallel robot Delta of SIG Demau- rex SA (invented at EPFL [140]) during packaging of chocolates. Introduction 9 on where it is bolted down. In contrast, a mobile robot would be able to travel throughout the manufacturing plant, flexibly applying its talents wherever it is most effective. This book focuses on the technology of mobility: how can a mobile robot move unsu- pervised through real-world environments to fulfill its tasks? The first challenge is locomo- tion itself. How should a mobile robot move, and what is it about a particular locomotion mechanism that makes it superior to alternative locomotion mechanisms? Hostile environments such as Mars trigger even more unusual locomotion mechanisms (figure 1.2). In dangerous and inhospitable environments, even on Earth, such teleoperated systems have gained popularity (figures 1.3, 1.4, 1.5, 1.6). In these cases, the low-level complexities of the robot often make it impossible for a human operator to directly control its motions. The human performs localization and cognition activities, but relies on the robot’s control scheme to provide motion control. For example, Plustech’s walking robot provides automatic leg coordination while the human operator chooses an overall direction of travel (figure 1.3). Figure 1.6 depicts an underwater vehicle that controls six propellers to autonomously stabilize the robot subma- rine in spite of underwater turbulence and water currents while the operator chooses posi- tion goals for the submarine to achieve. Other commercial robots operate not where humans cannot go but rather share space with humans in human environments (figure 1.7). These robots are compelling not for rea- sons of mobility but because of their autonomy, and so their ability to maintain a sense of position and to navigate without human intervention is paramount. Figure 1.2 The mobile robot Sojourner was used during the Pathfinder mission to explore Mars in summer 1997. It was almost completely teleoperated from Earth. However, some on-board sensors allowed for obstacle detection. (http://ranier.oact.hq.nasa.gov/telerobotics_page/telerobotics.shtm). ? NASA/JPL Figure 1.3 Plustech developed the first application-driven walking robot. It is designed to move wood out of the forest. The leg coordination is automated, but navigation is still done by the human operator on the robot. (http://www.plustech.fi). ? Plustech. Figure 1.4 Airduct inspection robot featuring a pan-tilt camera with zoom and sensors for automatic inclination control, wall following, and intersection detection (http://asl.epfl.ch). ? Sedirep / EPFL. Figure 1.5 Picture of Pioneer, a robot designed to explore the Sarcophagus at Chernobyl. ? Wide World Photos. Figure 1.6 Picture of recovering MBARI’s ALTEX AUV (autonomous underwater vehicle) onto the Icebreaker Healy following a dive beneath the Arctic ice. Todd Walsh ? 2001 MBARI. Figure 1.7 Tour-guide robots are able to interact and present exhibitions in an educational way [48, 118, 132, 143,]. Ten Roboxes have operated during 5 months at the Swiss exhibition EXPO.02, meeting hun- dreds of thousands of visitors. They were developed by EPFL [132] (http://robotics.epfl.ch) and com- mercialized by BlueBotics (http://www.bluebotics.ch). Figure 1.8 Newest generation of the autonomous guided vehicle (AGV) of SWISSLOG used to transport motor blocks from one assembly station to another. It is guided by an electrical wire installed in the floor. There are thousands of AGVs transporting products in industry, warehouses, and even hospitals. ? Swisslog. front back Figure 1.9 HELPMATE is a mobile robot used in hospitals for transportation tasks. It has various on-board sen- sors for autonomous navigation in the corridors. The main sensor for localization is a camera looking to the ceiling. It can detect the lamps on the ceiling as references, or landmarks (http:// www.pyxis.com). ? Pyxis Corp. Figure 1.10 BR 700 industrial cleaning robot (left) and the RoboCleaner RC 3000 consumer robot developed and sold by Alfred K?rcher GmbH & Co., Germany. The navigation system of BR 700 is based on a very sophisticated sonar system and a gyro. The RoboCleaner RC 3000 covers badly soiled areas with a special driving strategy until it is really clean. Optical sensors measure the degree of pollution of the aspirated air (http://www.karcher.de). ? Alfred K?rcher GmbH & Co. Figure 1.11 PIONEER is a modular mobile robot offering various options like a gripper or an on-board camera. It is equipped with a sophisticated navigation library developed at SRI, Stanford, CA (Reprinted with permission from ActivMedia Robotics, http://www.MobileRobots.com). Figure 1.12 B21 of iRobot is a sophisticated mobile robot with up to three Intel Pentium processors on board. It has a large variety of sensors for high-performance navigation tasks (http://www.irobot.com/rwi/). ? iRobot Inc. Figure 1.13 KHEPERA is a small mobile robot for research and education. It is only about 60 mm in diameter. Various additional modules such as cameras and grippers are available. More then 700 units had already been sold by the end of 1998. KHEPERA is manufactured and distributed by K-Team SA, Switzerland (http://www.k-team.com). ? K-Team SA. For example, AGV (autonomous guided vehicle) robots (figure 1.8) autonomously deliver parts between various assembly stations by following special electrical guidewires using a custom sensor. The Helpmate service robot transports food and medication throughout hospitals by tracking the position of ceiling lights, which are manually specified to the robot beforehand (figure 1.9). Several companies have developed autonomous clean- ing robots, mainly for large buildings (figure 1.10). One such cleaning robot is in use at the Paris Metro. Other specialized cleaning robots take advantage of the regular geometric pat- tern of aisles in supermarkets to facilitate the localization and navigation tasks. Research into high-level questions of cognition, localization, and navigation can be per- formed using standard research robot platforms that are tuned to the laboratory environ- ment. This is one of the largest current markets for mobile robots. Various mobile robot platforms are available for programming, ranging in terms of size and terrain capability. The most popular research robots are those of ActivMedia Robotics, K-Team SA, and I- Robot (figures 1.11, 1.12, 1.13) and also very small robots like the Alice from EPFL (Swiss Federal Institute of Technology at Lausanne) (figure 1.14). Although mobile robots have a broad set of applications and markets as summarized above, there is one fact that is true of virtually every successful mobile robot: its design involves the integration of many different bodies of knowledge. No mean feat, this makes mobile robotics as interdisciplinary a field as there can be. To solve locomotion problems, the mobile roboticist must understand mechanism and kinematics; dynamics and control theory. To create robust perceptual systems, the mobile roboticist must leverage the fields of signal analysis and specialized bodies of knowledge such as computer vision to properly employ a multitude of sensor technologies. Localization and navigation demand knowl- edge of computer algorithms, information theory, artificial intelligence, and probability theory. Figure 1.15 depicts an abstract control scheme for mobile robot systems that we will use throughout this text. This figure identifies many of the main bodies of knowledge associ- ated with mobile robotics. This book provides an introduction to all aspects of mobile robotics, including software and hardware design considerations, related technologies, and algorithmic techniques. The intended audience is broad, including both undergraduate and graduate students in intro- ductory mobile robotics courses, as well as individuals fascinated by the field. While not absolutely required, a familiarity with matrix algebra, calculus, probability theory, and computer programming will s