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Introduction to Autonomous Mobile Robots(自主移动机器人导论第二版 英文)

Introduction to Autonomous Mobile Robots(自主移动机器人导论第二版 英文) This book is the result of inspirations and contributions from many researchers and students at the Swiss Federal Institutes of Technology Zurich (ETH) and Lausanne (EPFL), Carnegie Mellon University’s Robotics Institute, Pittsburgh (CMU), and many others around the globe. We would like to thank all the researchers in mobile robotics who make this field so rich and stimulating by sharing their goals and visions with the community. It is their work that enabled us to colle ct the material for this book.

Mobile robots range from the Mars Pathfinder mission's teleoperated Sojourner to the cleaning robots in the Paris Metro. This text offers students and other interested readers an introduction to the fundamentals of mobile robotics, spanning the mechanical, motor, sensory, perceptual, and cognitive layers the field comprises. The text focuses on mobility itself, offering an overview of the mechanisms that allow a mobile robot to move through a real world environment to perform its tasks, including locomotion, sensing, localization, and motion planning. It synthesizes material from such fields as kinematics, control theory, signal analysis, computer vision, information theory, artificial intelligence, and probability theory. The book presents the techniques and technology that enable mobility in a series of interacting modules. Each chapter treats a different aspect of mobility, as the book moves from low-level to high-level details. It covers all aspects of mobile robotics, including software and hardware design considerations, related technologies, and algorithmic techniques.] This second edition has been revised and updated throughout, with 130 pages of new material on such topics as locomotion, perception, localization, and planning and navigation. Problem sets have been added at the end of each chapter. Bringing together all aspects of mobile robotics into one volume, Introduction to Autonomous Mobile Robots can serve as a textbook or a working tool for beginning practitioners.
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stormsunshine 英文版,很好的资源。
2018-06-10
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qq_40570743 确实很好啊
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Introduction to Autonomous Mobile Robots_2ed
[网盘]Introduction to Autonomous Mobile Robots(中文清晰版)

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Introduction to Autonomous Mobile Robots(中文清晰版)

Introduction to Autonomous Mobile Robots,中文清晰版,经典的机器人学习书籍

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Introduction to Autonomous Mobile Robots 2nd.pdf

Introduction to Autonomous Mobile Robots 英文第二版。

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Introduction to Autonomous Mobile Robots(中文版)

This book is the result of inspirations and contributions from many researchers and students at the Swiss Federal Institutes of Technology Zurich (ETH) and Lausanne (EPFL), Carnegie Mellon University’s Robotics Institute, Pittsburgh (CMU), and many others around the globe. We would like to thank all the researchers in mobile robotics who make this field so rich and stimulating by sharing their goals and visions with the community. It is their work that enabled us to collect the material for this book.

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自主移动机器人导论中文版翻译PDF,画面比较清晰,可以阅读。

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introduction
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自主移动机器人导论二版(全英)

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 Legged Mobile Robots 17 2.2.1 Leg configurations and stability 18 2.2.2 Consideration of dynamics 21 2.2.3 Examples of legged robot locomotion 25 2.3 Wheeled Mobile Robots 35 2.3.1 Wheeled locomotion: The design space 35 2.3.2 Wheeled locomotion: Case studies 43 2.4 Aerial Mobile Robots 50 2.4.1 Introduction 50 2.4.2 Aircraft configurations 52 2.4.3 State of the art in autonomous VTOL 52 2.5 Problems 56 3 Mobile Robot Kinematics 57 3.1 Introduction 57 3.2 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: Robot kinematic models and constraints 73 3.3 Mobile Robot Maneuverability 77 3.3.1 Degree of mobility 77 3.3.2 Degree of steerability 81 3.3.3 Robot maneuverability 82 3.4 Mobile Robot Workspace 84 3.4.1 Degrees of freedom 84 3.4.2 Holonomic robots 85 3.4.3 Path and trajectory considerations 87 3.5 Beyond Basic Kinematics 90 3.6 Motion Control (Kinematic Control) 91 3.6.1 Open loop control (trajectory-following) 91 3.6.2 Feedback control 92 3.7 Problems 99 4 Perception 101 4.1 Sensors for Mobile Robots 101 4.1.1 Sensor classification 101 4.1.2 Characterizing sensor performance 103 4.1.3 Representing uncertainty 109 4.1.4 Wheel/motor sensors 115 4.1.5 Heading sensors 116 4.1.6 Accelerometers 119 4.1.7 Inertial measurement unit (IMU) 121 4.1.8 Ground beacons 122 4.1.9 Active ranging 125 4.1.10 Motion/speed sensors 140 4.1.11 Vision sensors 142 4.2 Fundamentals of Computer Vision 142 4.2.1 Introduction 142 4.2.2 The digital camera 142 4.2.3 Image formation 148 4.2.4 Omnidirectional cameras 159 4.2.5 Structure from stereo 169 4.2.6 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 Feature Extraction 208 4.5 Image Feature Extraction: Interest Point Detectors 212 4.5.1 Introduction 212 4.5.2 Properties of the ideal feature detector 213 4.5.3 Corner detectors 215 4.5.4 Invariance to photometric and geometric changes 220 4.5.5 Blob detectors 227 4.6 Place Recognition 234 4.6.1 Introduction 234 4.6.2 From bag of features to visual words 235 4.6.3 Efficient location recognition by using an inverted file 236 4.6.4 Geometric verification for robust place recognition 237 4.6.5 Applications 237 4.6.6 Other image representations for place recognition 238 4.7 Feature Extraction Based on Range Data (Laser, Ultrasonic) 242 4.7.1 Line fitting 243 4.7.2 Six line-extraction algorithms 248 4.7.3 Range histogram features 259 4.7.4 Extracting other geometric features 260 4.8 Problems 262 5 Mobile Robot Localization 265 5.1 Introduction 265 5.2 The Challenge of Localization: Noise and Aliasing 266 5.2.1 Sensor noise 267 5.2.2 Sensor aliasing 268 5.2.3 Effector noise 269 5.2.4 An error model for odometric position estimation 270 5.3 To Localize or Not to Localize: Localization-Based Navigation Versus Programmed Solutions 275 5.4 Belief Representation 278 5.4.1 Single-hypothesis belief 278 5.4.2 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 5.6 Probabilistic Map-Based Localization 296 5.6.1 Introduction 296 5.6.2 The robot localization problem 297 5.6.3 Basic concepts of probability theory 299 5.6.4 Terminology 302 5.6.5 The ingredients of probabilistic map-based localization 304 5.6.6 Classification of localization problems 306 5.6.7 Markov localization 307 5.6.8 Kalman filter localization 322 5.7 Other Examples of Localization Systems 342 5.7.1 Landmark-based navigation 344 5.7.2 Globally unique localization 345 5.7.3 Positioning beacon systems 346 5.7.4 Route-based localization 347 5.8 Autonomous Map Building 348 5.8.1 Introduction 348 5.8.2 SLAM: The simultaneous localization and mapping problem 349 5.8.3 Mathematical definition of SLAM 351 5.8.4 Extended Kalman Filter (EKF) SLAM 353 5.8.5 Visual SLAM with a single camera 356 5.8.6 Discussion on EKF SLAM 359 5.8.7 Graph-based SLAM 361 5.8.8 Particle filter SLAM 363 5.8.9 Open challenges in SLAM 364 5.8.10 Open source SLAM software and other resources 365 5.9 Problems 366 6 Planning and Navigation 369 6.1 Introduction 369 6.2 Competences for Navigation: Planning and Reacting 370 6.3 Path Planning 371 6.3.1 Graph search 373 6.3.2 Potential field path planning 386 6.4 Obstacle avoidance 393 6.4.1 Bug algorithm 393 Contents xi 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 6.4.7 Nearness diagram 405 6.4.8 Gradient method 405 6.4.9 Adding dynamic constraints 406 6.4.10 Other approaches 406 6.4.11 Overview 406 6.5 Navigation Architectures 409 6.5.1 Modularity for code reuse and sharing 410 6.5.2 Control localization 410 6.5.3 Techniques for decomposition 411 6.5.4 Case studies: tiered robot architectures 416 6.6 Problems 423

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自主移动机器人导论(高清PDF)

序言 第1章 引言 1.1 引言 1.2 本书综述 第2章 运动 2.1 引言 2.1.1 运动的关键问题 2.2 腿式移动机器人 2.2.1 腿的构造与稳定性 2.2.2 腿式机器人运动的例子 2.3 轮式移动机器人 2.3.1 轮子运动:设计空间 2.3.2 轮子运动:实例研究 第3章 移动机器人运动学 3.1 引言 3.2 运动学模型和约束 3.2.1 表示机器人的位置 3.2.2 前向运动学模型 3.2.3 轮子运动学约束 3.2.4 机器人运动学约束 3.2.5 举例:机器人运动学模型和约束 3.3 移动机器人的机动性 3.3.1 活动性的程度 3.3.2 可操纵度 3.3.3 机器人的机动性 3.4 移动机器人工作空间 3.4.1 自由度 3.4.2 完整机器人 3.4.3 路径和轨迹的考虑 3.5 基本运动学之外 3.6 运动控制 3.6.1 开环控制 3.6.2 反馈控制 第4章 感知 4.1 移动机器人的传感器 4.1.1 传感器分类 4.1.2 表征传感器的特性指标 4.1.3 轮子/电机传感器 4.1.4 导向传感器 4.1.5 基于地面的信标 4.1.6 有源测距 4.1.7 运动/速度传感器 4.1.8 基于视觉的传感器 4.2 表示不确定性 4.2.1 统计的表示 4.2.2 误差传播:对不确定的测量进行组合 4.3 特征提取 4.3.1 基于距离数据的特征提取(激光、超声和基于视觉测距) 4.3.2 基于可视表象的特征提取 第5章 移动机器人的定位 5.1 引言 5.2 定位的挑战:噪声和混叠 5.2.1 传感器噪声 5.2.2 传感器混叠 5.2.3 执行器噪声 5.2.4 里程表位置估计的误差模型 5.3 定位或不定位:基于定位的导航与编程求解的对比 5.4 信任度的表示 5.4.1 单假设信任度 5.4.2 多假设信任度 5.5 地图表示方法 5.5.1 连续的表示方法 5.5.2 分解策略 5.5.3 发展水平:地图表示方法的最新挑战 5.6 基于概率地图的定位 5.6.1 引言 5.6.2 马尔可夫定位 5.6.3 卡尔曼滤波器定位 5.7 定位系统的其他例子 5.7.1 基于路标的导航 5.7.2 全局唯一定位 5.7.3 定位信标系统 5.7.4 基于路由的定位 5.8 自主地图的构建 5.8.1 随机构图的技术 5.8.2 其他的构图技术 第6章 规划与导航 6.1 引言 6.2 导航能力:规划和反应 6.2.1 路径规划 6.2.2 避障 6.3 导航的体系结构 6.3.1 代码重用与共享的模块性 6.3.2 控制定位 6.3.3 分解技术 6.3.4 实例研究:分层机器人结构 参考文献

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