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人工智能机器人学导论（第二版）（英文版） 
定 价：139 元 丛书名：国外计算机科学教材系列
9 7 3 8 7 7 2 1 3 2 3 1 9 读者对象:本书可作为机器人工程、人工智能、电子、自动化及计算机等专业高年级本科生和研究生的教材或参考书，也可供从事智能机器人方面研究的教师和研究人员，以及参加各类机器人学科竞赛的指导教师和学生学习参考。 
本书首先介绍人工智能机器人的定义、历史和体系结构，然后全面系统地阐述人工智能机器人在传感、感知、运动、规划、导航、学习、交互等方面的基础理论和关键技术。全书共分为五部分。第一部分共5章，定义了什么是智能机器人，介绍了人工智能机器人简史，并讨论了自动化与自治、软件体系结构和遥操作；第二部分共6章，针对机器人的反应（行为）层智能展开讨论，分别对应机器人行为、感知与行为、行为协调、运动学、传感器与感知，以及距离感知等方面的内容；第三部分共5章，详细讨论机器人的慎思层智能，包括慎思层的内涵、导航、路径和动作规划、定位、建图与探索，以及机器学习等内容；第四部分共2章，讨论机器人的交互层智能，包括多机器人系统和人-机器人交互；第五部分共2章，分别介绍自治系统的设计与评估方法，以及与机器人相关的伦理问题。
分别于1980年、1989年和1992年在美国佐治亚理工学院获得机械工程学学士学位、计算机科学硕士和博士学位，现任德克萨斯农工大学计算机科学与工程系的Raytheon荣誉教授，机器人辅助搜索与救援研究中心主任，IEEE会士，曾任IEEE机器人和自动化执行委员会执委。研究方向为人工智能，人-机器人交互，以及异构多机器人系统。已发表100多部/篇出版物，是国际上救援机器人和人-机器人交互领域的开创者之一。
Robin R. Murphy分别于1980年、1989年和1992年在美国佐治亚理工学院获得机械工程学学士学位、计算机科学硕士和博士学位，现任得克萨斯农工大学计算机科学与工程系Raytheon荣誉教授，机器人辅助搜索与救援研究中心主任。IEEE会士，曾任IEEE机器人和自动化执行委员会执委。研究方向为人工智能、人-机器人交互，以及异构多机器人系统。已发表100多部/篇出版物，是国际范围内救援机器人和人-机器人交互领域的开创者之一。
I Framework for Thinking About AI and Robotics
1 What Are Intelligent Robots? 1.1 Overview 1.2 Definition: What Is an Intelligent Robot? 1.3 What Are the Components of a Robot? 1.4 Three Modalities: What Are the Kinds of Robots? 1.5 Motivation: Why Robots? 1.6 Seven Areas of AI: Why Intelligence? 1.7 Summary 1.8 Exercises 1.9 End Notes 2 A Brief History of AI Robotics 2.1 Overview 2.2 Robots as Tools, Agents, or Joint Cognitive Systems 2.3 World War II and the Nuclear Industry 2.4 Industrial Manipulators 2.5 Mobile Robots 2.6 Drones 2.7 The Move to Joint Cognitive Systems 2.8 Summary 2.9 Exercises 2.10 End Notes 3 Automation and Autonomy 3.1 Overview 3.2 The Four Sliders of Autonomous Capabilities 3.2.1 Plans: Generation versus Execution 3.2.2 Actions: Deterministic versus Non-deterministic 3.2.3 Models: Open- versus Closed-World 3.2.4 Knowledge Representation: Symbols versus Signals 3.3 Bounded Rationality 3.4 Impact of Automation and Autonomy 3.5 Impact on Programming Style 3.6 Impact on Hardware Design 3.7 Impact on Types of Functional Failures 3.7.1 Functional Failures 3.7.2 Impact on Types of Human Error 3.8 Trade-Spaces in Adding Autonomous Capabilities 3.9 Summary 3.10 Exercises 3.11 End Notes 4 Software Organization of Autonomy 4.1 Overview 4.2 The Three Types of Software Architectures 4.2.1 Types of Architectures 4.2.2 Architectures Reinforce Good Software Engineering Principles 4.3 Canonical AI Robotics Operational Architecture 4.3.1 Attributes for Describing Layers 4.3.2 The Reactive Layer 4.3.3 The Deliberative Layer 4.3.4 The Interactive Layer 4.3.5 Canonical Operational Architecture Diagram 4.4 Other Operational Architectures 4.4.1 Levels of Automation 4.4.2 Autonomous Control Levels (ACL) 4.4.3 Levels of Initiative 4.5 Five Subsystems in Systems Architectures 4.6 Three Systems Architecture Paradigms 4.6.1 Trait 1: Interaction Between Primitives 4.6.2 Trait 2: Sensing Route 4.6.3 Hierarchical Systems Architecture Paradigm 4.6.4 Reactive Systems Paradigm 4.6.5 Hybrid Deliberative/Reactive Systems Paradigm 4.7 Execution Approval and Task Execution 4.8 Summary 4.9 Exercises 4.10 End Notes 5 Telesystems 5.1 Overview 5.2 Taskable Agency versus Remote Presence 5.3 The Seven Components of a Telesystem 5.4 Human Supervisory Control 5.4.1 Types of Supervisory Control 5.4.2 Human Supervisory Control for Telesystems 5.4.3 Manual Control 5.4.4 Traded Control 5.4.5 Shared Control 5.4.6 Guarded Motion 5.5 Human Factors 5.5.1 Cognitive Fatigue 5.5.2 Latency 5.5.3 Human: Robot Ratio 5.5.4 Human Out-of-the-Loop Control Problem 5.6 Guidelines for Determining if a Telesystem Is Suitable for an Application 5.6.1 Examples of Telesystems 5.7 Summary 5.8 Exercises 5.9 End Notes II Reactive Functionality 6 Behaviors 6.1 Overview 6.2 Motivation for Exploring Animal Behaviors 6.3 Agency and Marr’s Computational Theory 6.4 Example of Computational Theory: Rana Computatrix 6.5 Animal Behaviors 6.5.1 Reflexive Behaviors 6.6 Schema Theory 6.6.1 Schemas as Objects 6.6.2 Behaviors and Schema Theory 6.6.3 S-R: Schema Notation 6.7 Summary 6.8 Exercises 6.9 End Notes 7 Perception and Behaviors 7.1 Overview 7.2 Action-Perception Cycle 7.3 Gibson: Ecological Approach 7.3.1 Optic Flow 7.3.2 Nonvisual Affordances 7.4 Two Perceptual Systems 7.5 Innate Releasing Mechanisms 7.5.1 Definition of Innate Releasing Mechanisms 7.5.2 Concurrent Behaviors 7.6 Two Functions of Perception 7.7 Example: Cockroach Hiding 7.7.1 Decomposition 7.7.2 Identifying Releasers 7.7.3 Implicit versus Explicit Sequencing 7.7.4 Perception 7.7.5 Architectural Considerations 7.8 Summary 7.9 Exercises 7.10 End Notes 8 Behavioral Coordination 8.1 Overview 8.2 Coordination Function 8.3 Cooperating Methods: Potential Fields 8.3.1 Visualizing Potential Fields 8.3.2 Magnitude Profiles 8.3.3 Potential Fields and Perception 8.3.4 Programming a Single Potential Field 8.3.5 Combination of Fields and Behaviors 8.3.6 Example Using One Behavior per Sensor 8.3.7 Advantages and Disadvantages 8.4 Competing Methods: Subsumption 8.4.1 Example 8.5 Sequences: Finite State Automata 8.5.1 A Follow the Road FSA 8.5.2 A Pick Up the Trash FSA 8.6 Sequences: Scripts 8.7 AI and Behavior Coordination 8.8 Summary 8.9 Exercises 8.10 End Notes 9 Locomotion 9.1 Overview 9.2 Mechanical Locomotion 9.2.1 Holonomic versus Nonholonomic 9.2.2 Steering 9.3 Biomimetic Locomotion 9.4 Legged Locomotion 9.4.1 Number of Leg Events 9.4.2 Balance 9.4.3 Gaits 9.4.4 Legs with Joints 9.5 Action Selection 9.6 Summary 9.7 Exercises 9.8 End Notes 10 Sensors and Sensing 10.1 Overview 10.2 Sensor and Sensing Model 10.2.1 Sensors: Active or Passive 10.2.2 Sensors: Types of Output and Usage 10.3 Odometry, Inertial Navigation System (INS) and Global Positioning System (GPS) 10.4 Proximity Sensors 10.5 Computer Vision 10.5.1 Computer Vision Definition 10.5.2 Grayscale and Color Representation 10.5.3 Region Segmentation 10.5.4 Color Histogramming 10.6 Choosing Sensors and Sensing 10.6.1 Logical Sensors 10.6.2 Behavioral Sensor Fusion 10.6.3 Designing a Sensor Suite 10.7 Summary 10.8 Exercises 10.9 End Notes 11 Range Sensing 11.1 Overview 11.2 Stereo 11.3 Depth from X 11.4 Sonar or Ultrasonics 11.4.1 Light Stripers 11.4.2 Lidar 11.4.3 RGB-D Cameras 11.4.4 Point Clouds 11.5 Case Study: Hors d’Oeuvres, Anyone? 11.6 Summary 11.7 Exercises 11.8 End Notes III Deliberative Functionality 12 Deliberation 12.1 Overview 12.2 Strips 12.2.1 More Realistic Strips Example 12.2.2 Strips Summary 12.2.3 Revisiting the Closed-World Assumption and the Frame Problem 12.3 Symbol Grounding Problem 12.4 GlobalWorld Models 12.4.1 Local Perceptual Spaces 12.4.2 Multi-level or HierarchicalWorld Models 12.4.3 Virtual Sensors 12.4.4 Global World Model and Deliberation 12.5 Nested Hierarchical Controller 12.6 RAPS and 3T 12.7 Fault Detection Identification and Recovery 12.8 Programming Considerations 12.9 Summary 12.10 Exercises 12.11 End Notes 13 Navigation 13.1 Overview 13.2 The Four Questions of Navigation 13.3 Spatial Memory 13.4 Types of Path Planning 13.5 Landmarks and Gateways 13.6 Relational Methods 13.6.1 Distinctive Places 13.6.2 Advantages and Disadvantages 13.7 Associative Methods 13.8 Case Study of Topological Navigation with a Hybrid Architecture 13.8.1 Topological Path Planning 13.8.2 Navigation Scripts 13.8.3 Lessons Learned 13.9 Discussion of Opportunities for AI 13.10 Summary 13.11 Exercises 13.12 End Notes 14 Metric Path Planning and Motion Planning 14.1 Overview 14.2 Four Situations Where Topological Navigation Is Not Sufficient 14.3 Configuration Space 14.3.1 Meadow Maps 14.3.2 Generalized Voronoi Graphs 14.3.3 Regular Grids 14.3.4 Quadtrees 14.4 Metric Path Planning 14.4.1 A* and Graph-Based Planners 14.4.2 Wavefront-Based Planners 14.5 Executing a Planned Path 14.5.1 Subgoal Obsession 14.5.2 Replanning 14.6 Motion Planning 14.7 Criteria for Evaluating Path and Motion Planners 14.8 Summary 14.9 Exercises 14.10 End Notes 15 Localization, Mapping, and Exploration 15.1 Overview 15.2 Localization 15.3 Feature-Based Localization 15.4 Iconic Localization 15.5 Static versus Dynamic Environments 15.6 Simultaneous Localization and Mapping 15.7 Terrain Identification and Mapping 15.7.1 Digital Terrain Elevation Maps 15.7.2 Terrain Identification 15.7.3 Stereophotogrammetry 15.8 Scale and Traversability 15.8.1 Scale 15.8.2 Traversability Attributes 15.9 Exploration 15.9.1 Reactive Exploration 15.9.2 Frontier-Based Exploration 15.9.3 Generalized Voronoi Graph Methods 15.10 Localization, Mapping, Exploration, and AI 15.11 Summary 15.12 Exercises 15.13 End Notes 16 Learning 16.1 Overview 16.2 Learning 16.3 Types of Learning by Example 16.4 Common Supervised Learning Algorithms 16.4.1 Induction 16.4.2 Support Vector Machines 16.4.3 Decision Trees 16.5 Common Unsupervised Learning Algorithms 16.5.1 Clustering 16.5.2 Artificial Neural Networks 16.6 Reinforcement Learning 16.6.1 Utility Functions 16.6.2 Q-learning 16.6.3 Q-learning Example 16.6.4 Q-learning Discussion 16.7 Evolutionary Robotics and Genetic Algorithms 16.8 Learning and Architecture 16.9 Gaps and Opportunities 16.10 Summary 16.11 Exercises 16.12 End Notes IV Interactive Functionality 17 MultiRobot Systems (MRS) 17.1 Overview 17.2 Four Opportunities and Seven Challenges 17.2.1 Four Advantages of MRS 17.2.2 Seven Challenges in MRS 17.3 Multirobot Systems and AI 17.4 Designing MRS for Tasks 17.4.1 Time Expectations for a Task 17.4.2 Subject of Action 17.4.3 Movement 17.4.4 Dependency 17.5 Coordination Dimension of MRS Design 17.6 Systems Dimensions in Design 17.6.1 Communication 17.6.2 MRS Composition 17.6.3 Team Size 17.7 Five Most Common Occurrences of MRS 17.8 Operational Architectures for MRS 17.9 Task Allocation 17.10 Summary 17.11 Exercises 17.12 End Notes 18 Human-Robot Interaction 18.1 Overview 18.2 Taxonomy of Interaction 18.3 Contributions from HCI, Psychology, Communications 18.3.1 Human-Computer Interaction 18.3.2 Psychology 18.3.3 Communications 18.4 User Interfaces 18.4.1 Eight Golden Rules for User Interface Design 18.4.2 Situation Awareness 18.4.3 Multiple Users 18.5 Modeling Domains, Users, and Interactions 18.5.1 Motivating Example of Users and Interactions 18.5.2 Cognitive Task Analysis 18.5.3 CognitiveWork Analysis 18.6 Natural Language and Naturalistic User Interfaces 18.6.1 Natural Language Understanding 18.6.2 Semantics and Communication 18.6.3 Models of the Inner State of the Agent 18.6.4 Multi-modal Communication 18.7 Human-Robot Ratio 18.8 Trust 18.9 Testing and Metrics 18.9.1 Data Collection Methods 18.9.2 Metrics 18.10 Human-Robot Interaction and the Seven Areas of Artificial Intelligence 18.11 Summary 18.12 Exercises 18.13 End Notes V Design and the Ethics of Building Intelligent Robots 19 Designing and Evaluating Autonomous Systems 19.1 Overview 19.2 Designing a Specific Autonomous Capability 19.2.1 Design Philosophy 19.2.2 Five Questions for Designing an Autonomous Robot 19.3 Case Study: Unmanned Ground Robotics Competition 19.4 Taxonomies and Metrics versus System Design 19.5 Holistic Evaluation of an Intelligent Robot 19.5.1 Failure Taxonomy 19.5.2 Four Types of Experiments 19.5.3 Data to Collect 19.6 Case Study: Concept Experimentation 19.7 Summary 19.8 Exercises 19.9 End Notes 20 Ethics 20.1 Overview 20.2 Types of Ethics 20.3 Categorizations of Ethical Agents 20.3.1 Moor’s Four Categories 20.3.2 Categories of Morality 20.4 Programming Ethics 20.4.1 Approaches from Philosophy 20.4.2 Approaches from Robotics 20.5 Asimov’s Three Laws of Robotics 20.5.1 Problems with the Three Laws 20.5.2 The Three Laws of Responsible Robotics 20.6 Artificial Intelligence and Implementing Ethics 20.7 Summary 20.8 Exercises 20.9 End Notes Bibliography Index
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