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[已搜无重复]Introduction to Autonomous Mobile Robots
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非常经典的一部关于移动机器人的著作，来自于Roland Siegwart, Illah Reza Nourbakhsh, Davide Scaramuzza ，适合初学者学习，也适合此领域的研究者参考。![[已搜无重复]Introduction to Autonomous Mobile Robots](https://muchongimg.xmcimg.com/data/bcs/2014/0218/w67h2066576_1392657600_809.jpg#opennewwindow) 目录如下 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 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 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 5.6.1Introduction 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 |
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2014-02-18 01:28:36, 6.44 M
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