Introduction to Autonomous Mobile Robots

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1 Introduction to Autonomous Mobile Robots second edition Roland Siegwart, Illah R. Nourbakhsh, and Davide Scaramuzza The MIT Press Cambridge, Massachusetts London, England

Contents Acknowledgments xiii Preface xv 1 Introduction 1 1.1 Introduction 1 1.2 An Overview of the Book 11 2 Locomotion 13 2.1 Introduction 13 2.1.1 Key issues for locomotion 16 2.

2 Contents Acknowledgments xiii Preface xv 1 Introduction Introduction An Overview of the Book 11 2 Locomotion Introduction Key issues for locomotion Legged Mobile Robots Leg configurations and stability Consideration of dynamics Examples of legged robot locomotion Wheeled Mobile Robots Wheeled locomotion: The design space Wheeled locomotion: Case studies Aerial Mobile Robots / Introduction Aircraft configurations State of the art in autonomous VTOL Problems 56 3 Mobile Robot Kinematics Introduction Kinematic Models and Constraints 58

viii Contents 3.2.1 Representing robot position 58 3.2.2 Forward kinematic models 61 3.2.3 Wheel kinematic constraints 63 3.2.4 Robot kinematic constraints 71 3.2.5 Examples'.

3 viii Contents Representing robot position Forward kinematic models Wheel kinematic constraints Robot kinematic constraints Examples'. Robot kinematic models and constraints Mobile Robot Maneuverability Degree of mobility Degree of steerability Robot maneuverability Mobile Robot Workspace Degrees of freedom Holonomic robots Path and trajectory considerations Beyond Basic Kinematics Motion Control (Kinematic Control) Open loop control (trajectory-following) Feedback control Problems 99 Perception Sensors for Mobile Robots Sensor classification Characterizing sensor performance Representing uncertainty Wheel/motor sensors Heading sensors Accelerometers Inertial measurement unit (IMU) Ground beacons Active ranging Motion/speed sensors Vision sensors Fundamentals of Computer Vision Introduction The digital camera Image formation Omnidirectional cameras Structure from stereo Structure from motion 180

Contents ix 4.2.7 Motion and optical flow 189 4.2.8 Color tracking 192 4.3 Fundamentals of Image Processing 195 4.3.1 Image filtering 196 4.3.2 Edge detection 199 4.3.3 Computing image similarity 207 4.

4 Contents ix Motion and optical flow Color tracking Fundamentals of Image Processing Image filtering Edge detection Computing image similarity Feature Extraction Image Feature Extraction: Interest Point Detectors Introduction Properties of the ideal feature detector Corner detectors Invariance to photometric and geometric changes Blob detectors Place Recognition Introduction From bag of features to visual words Efficient location recognition by using an inverted file Geometric verification for robust place recognition Applications Other image representations for place recognition Feature Extraction Based on Range Data (Laser, Ultrasonic) Line fitting Six line-extraction algorithms Range histogram features Extracting other geometric features Problems 262 Mobile Robot Localization Introduction The Challenge of Localization: Noise and Aliasing Sensor noise, Sensor aliasing Effector noise An error model for odometric position estimation To Localize or Not to Localize: Localization-Based Navigation Versus Programmed Solutions Belief Representation Single-hypothesis belief Multiple-hypothesis belief 280

X Contents 5.5 Map Representation 284 5.5.1 Continuous representations 284 5.5.2 Decomposition strategies 287 5.5.3 State of the art: Current challenges in map representation 294 5.

5 X Contents 5.5 Map Representation Continuous representations Decomposition strategies State of the art: Current challenges in map representation Probabilistic Map-Based Localization Introduction The robot localization problem Basic concepts of probability theory Terminology/ The ingredients of probabilistic map-based localization Classification of localization problems Markov localization Kaiman filter localization Other Examples of Localization Systems Landmark-based navigation Globally unique localization Positioning beacon systems Route-based localization Autonomous Map Building Introduction SLAM: The simultaneous localization and mapping problem Mathematical definition of SLAM Extended Kaiman Filter (EKF) SLAM Visual SLAM with a single camera Discussion on EKF SLAM Graph-based SLAM Particle filter SLAM Open challenges in SLAM Open source SLAM software and other resources Problems 366 Planning and Navigation Introduction Competences for Navigation: Planning and Reacting Path Planning Graph search Potential field path planning Obstacle avoidance Bug algorithm 393

Contents 6.4.2 Vector field histogram 397 6.4.3 The bubble band technique 399 6.4.4 Curvature velocity techniques 401 6.4.5 Dynamic window approaches 402 6.4.6 The Schlegel approach to obstacle avoidance 404 6.

6 Contents Vector field histogram The bubble band technique Curvature velocity techniques Dynamic window approaches The Schlegel approach to obstacle avoidance Nearness diagram Gradient method Adding dynamic constraints Other approaches Overview Navigation Architectures Modularity for code reuse and sharing Control localization Techniques for decomposition Case studies: tiered robot architectures Problems 423 Bibliography 425 Books 425 Papers 427 Referenced Webpages 444 Index 447 /

Mobile Robotics. Mathematics, Models, and Methods. HI Cambridge. Alonzo Kelly. Carnegie Mellon University UNIVERSITY PRESS

Mobile Robotics. Mathematics, Models, and Methods. HI Cambridge. Alonzo Kelly. Carnegie Mellon University UNIVERSITY PRESS Mobile Robotics Mathematics, Models, and Methods Alonzo Kelly Carnegie Mellon University HI Cambridge UNIVERSITY PRESS Contents Preface page xiii 1 Introduction 1 1.1 Applications of Mobile Robots 2 1.2