Introduction to AI Robotics, Second

by Murphy

| ISBN: 9780262038485 | Copyright 0

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A comprehensive survey of artificial intelligence algorithms and programming organization for robot systems, combining theoretical rigor and practical applications.

This textbook offers a comprehensive survey of artificial intelligence (AI) algorithms and programming organization for robot systems. Readers who master the topics covered will be able to design and evaluate an artificially intelligent robot for applications involving sensing, acting, planning, and learning. A background in AI is not required; the book introduces key AI topics from all AI subdisciplines throughout the book and explains how they contribute to autonomous capabilities.

This second edition is a major expansion and reorganization of the first edition, reflecting the dramatic advances made in AI over the past fifteen years. An introductory overview provides a framework for thinking about AI for robotics, distinguishing between the fundamentally different design paradigms of automation and autonomy. The book then discusses the reactive functionality of sensing and acting in AI robotics; introduces the deliberative functions most often associated with intelligence and the capability of autonomous initiative; surveys multi-robot systems and (in a new chapter) human-robot interaction; and offers a “metaview” of how to design and evaluate autonomous systems and the ethical considerations in doing so. New material covers locomotion, simultaneous localization and mapping,  human-robot interaction, machine learning, and ethics. Each chapter includes exercises, and many chapters provide case studies. Endnotes point to additional reading, highlight advanced topics, and offer robot trivia.

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Brief Contents (pg. vii)
Contents (pg. ix)
Preface (pg. xix)
I. Framework for Thinking About AI and Robotics (pg. 1)
1: What Are Intelligent Robots? (pg. 3)
1.1 Overview (pg. 3)
1.2 Definition: What Is an Intelligent Robot? (pg. 4)
1.3 What Are the Components of a Robot? (pg. 7)
1.4 Three Modalities: What Are the Kinds of Robots? (pg. 8)
1.5 Motivation: Why Robots? (pg. 11)
1.6 Seven Areas of AI: Why Intelligence? (pg. 13)
1.7 Summary (pg. 15)
1.8 Exercises (pg. 16)
1.9 End Notes (pg. 17)
2: A Brief History of AI Robotics (pg. 19)
2.1 Overview (pg. 19)
2.2 Robots as Tools, Agents, or Joint Cognitive Systems (pg. 20)
2.3 World War II and the Nuclear Industry (pg. 21)
2.4 Industrial Manipulators (pg. 24)
2.5 Mobile Robots (pg. 29)
2.6 Drones (pg. 35)
2.7 The Move to Joint Cognitive Systems (pg. 36)
2.8 Summary (pg. 37)
2.9 Exercises (pg. 38)
2.10 End Notes (pg. 38)
3: Automation and Autonomy (pg. 41)
3.1 Overview (pg. 41)
3.2 The Four Sliders of Autonomous Capabilities (pg. 43)
3.3 Bounded Rationality (pg. 48)
3.4 Impact of Automation and Autonomy (pg. 49)
3.5 Impact on Programming Style (pg. 50)
3.6 Impact on Hardware Design (pg. 50)
3.7 Impact on Types of Functional Failures (pg. 52)
3.8 Trade-Spaces in Adding Autonomous Capabilities (pg. 55)
3.9 Summary (pg. 57)
3.10 Exercises (pg. 59)
3.11 End Notes (pg. 61)
4: Software Organization of Autonomy (pg. 63)
4.1 Overview (pg. 64)
4.2 The Three Types of Software Architectures (pg. 65)
4.3 Canonical AI Robotics Operational Architecture (pg. 68)
4.4 Other Operational Architectures (pg. 75)
4.5 Five Subsystems in Systems Architectures (pg. 82)
4.6 Three Systems Architecture Paradigms (pg. 85)
4.7 Execution Approval and Task Execution (pg. 95)
4.8 Summary (pg. 97)
4.9 Exercises (pg. 100)
4.10 End Notes (pg. 101)
5: Telesystems (pg. 103)
5.1 Overview (pg. 104)
5.2 Taskable Agency versus Remote Presence (pg. 105)
5.3 The Seven Components of a Telesystem (pg. 105)
5.4 Human Supervisory Control (pg. 108)
5.5 Human Factors (pg. 116)
5.6 Guidelines for Determining if a Telesystem Is Suitable for an Application (pg. 122)
5.7 Summary (pg. 125)
5.8 Exercises (pg. 126)
5.9 End Notes (pg. 128)
II. Reactive Functionality (pg. 129)
6: Behaviors (pg. 131)
6.1 Overview (pg. 131)
6.2 Motivation for Exploring Animal Behaviors (pg. 132)
6.3 Agency and Marr's Computational Theory (pg. 134)
6.4 Example of Computational Theory: Rana Computatrix (pg. 137)
6.5 Animal Behaviors (pg. 141)
6.6 Schema Theory (pg. 143)
6.7 Summary (pg. 148)
6.8 Exercises (pg. 150)
6.9 End Notes (pg. 151)
7: Perception and Behaviors (pg. 153)
7.1 Overview (pg. 153)
7.2 Action-Perception Cycle (pg. 155)
7.3 Gibson: Ecological Approach (pg. 156)
7.4 Two Perceptual Systems (pg. 161)
7.5 Innate Releasing Mechanisms (pg. 162)
7.6 Two Functions of Perception (pg. 171)
7.7 Example: Cockroach Hiding (pg. 171)
7.8 Summary (pg. 178)
7.9 Exercises (pg. 181)
7.10 End Notes (pg. 182)
8: Behavioral Coordination (pg. 185)
8.1 Overview (pg. 185)
8.2 Coordination Function (pg. 186)
8.3 Cooperating Methods: Potential Fields (pg. 188)
8.4 Competing Methods: Subsumption (pg. 204)
8.5 Sequences: Finite State Automata (pg. 213)
8.6 Sequences: Scripts (pg. 220)
8.7 AI and Behavior Coordination (pg. 222)
8.8 Summary (pg. 223)
8.9 Exercises (pg. 224)
8.10 End Notes (pg. 226)
9: Locomotion (pg. 229)
9.1 Overview (pg. 229)
9.2 Mechanical Locomotion (pg. 230)
9.3 Biomimetic Locomotion (pg. 235)
9.4 Legged Locomotion (pg. 238)
9.5 Action Selection (pg. 245)
9.6 Summary (pg. 246)
9.7 Exercises (pg. 247)
9.8 End Notes (pg. 249)
10: Sensors and Sensing (pg. 251)
10.1 Overview (pg. 252)
10.2 Sensor and Sensing Model (pg. 253)
10.3 Odometry, Inertial Navigation System (INS) and Global Positioning System (GPS) (pg. 255)
10.4 Proximity Sensors (pg. 256)
10.5 Computer Vision (pg. 258)
10.6 Choosing Sensors and Sensing (pg. 269)
10.7 Summary (pg. 278)
10.8 Exercises (pg. 280)
10.9 End Notes (pg. 283)
11: Range Sensing (pg. 285)
11.1 Overview (pg. 285)
11.2 Stereo (pg. 288)
11.3 Depth from X (pg. 293)
11.4 Sonar or Ultrasonics (pg. 293)
11.5 Case Study: Hors d'Oeuvres, Anyone? (pg. 307)
11.6 Summary (pg. 315)
11.7 Exercises (pg. 315)
11.8 End Notes (pg. 317)
III. Deliberative Functionality (pg. 319)
12: Deliberation (pg. 321)
12.1 Overview (pg. 321)
12.2 Strips (pg. 323)
12.3 Symbol Grounding Problem (pg. 333)
12.4 Global World Models (pg. 335)
12.5 Nested Hierarchical Controller (pg. 339)
12.6 RAPS and 3T (pg. 342)
12.7 Fault Detection Identification and Recovery (pg. 346)
12.8 Programming Considerations (pg. 347)
12.9 Summary (pg. 348)
12.10 Exercises (pg. 349)
12.11 End Notes (pg. 351)
13: Navigation (pg. 353)
13.1 Overview (pg. 353)
13.2 The Four Questions of Navigation (pg. 355)
13.3 Spatial Memory (pg. 358)
13.4 Types of Path Planning (pg. 359)
13.5 Landmarks and Gateways (pg. 361)
13.6 Relational Methods (pg. 364)
13.7 Associative Methods (pg. 369)
13.8 Case Study of Topological Navigation with a Hybrid Architecture (pg. 369)
13.9 Discussion of Opportunities for AI (pg. 379)
13.10 Summary (pg. 381)
13.11 Exercises (pg. 382)
13.12 End Notes (pg. 384)
14: Metric Path Planning and Motion Planning (pg. 385)
14.1 Overview (pg. 385)
14.2 Four Situations Where Topological Navigation Is Not Sufficient (pg. 387)
14.3 Configuration Space (pg. 389)
14.4 Metric Path Planning (pg. 396)
14.5 Executing a Planned Path (pg. 402)
14.6 Motion Planning (pg. 407)
14.7 Criteria for Evaluating Path and Motion Planners (pg. 410)
14.8 Summary (pg. 411)
14.9 Exercises (pg. 413)
14.10 End Notes (pg. 415)
15: Localization, Mapping, and Exploration (pg. 417)
15.1 Overview (pg. 418)
15.2 Localization (pg. 419)
15.3 Feature-Based Localization (pg. 421)
15.4 Iconic Localization (pg. 423)
15.5 Static versus Dynamic Environments (pg. 424)
15.6 Simultaneous Localization and Mapping (pg. 424)
15.7 Terrain Identification and Mapping (pg. 426)
15.8 Scale and Traversability (pg. 432)
15.9 Exploration (pg. 435)
15.10 Localization, Mapping, Exploration, and AI (pg. 439)
15.11 Summary (pg. 441)
15.12 Exercises (pg. 442)
15.13 End Notes (pg. 443)
16:Learning (pg. 445)
16.1 Overview (pg. 446)
16.2 Learning (pg. 447)
16.3 Types of Learning by Example (pg. 449)
16.4 Common Supervised Learning Algorithms (pg. 450)
16.5 Common Unsupervised Learning Algorithms (pg. 454)
16.6 Reinforcement Learning (pg. 460)
16.7 Evolutionary Robotics and Genetic Algorithms (pg. 468)
16.8 Learning and Architecture (pg. 473)
16.9 Gaps and Opportunities (pg. 474)
16.10 Summary (pg. 475)
16.11 Exercises (pg. 476)
16.12 End Notes (pg. 478)
IV. Interactive Functionality (pg. 481)
17: MultiRobot Systems (MRS) (pg. 483)
17.1 Overview (pg. 484)
17.2 Four Opportunities and Seven Challenges (pg. 484)
17.3 Multirobot Systems and AI (pg. 487)
17.4 Designing MRS for Tasks (pg. 490)
17.5 Coordination Dimension of MRS Design (pg. 493)
17.6 Systems Dimensions in Design (pg. 494)
17.7 Five Most Common Occurrences of MRS (pg. 499)
17.8 Operational Architectures for MRS (pg. 501)
17.9 Task Allocation (pg. 503)
17.10 Summary (pg. 504)
17.11 Exercises (pg. 505)
17.12 End Notes (pg. 508)
18: Human-Robot Interaction (pg. 511)
18.1 Overview (pg. 512)
18.2 Taxonomy of Interaction (pg. 514)
18.3 Contributions from HCI, Psychology, Communications (pg. 516)
18.4 User Interfaces (pg. 518)
18.5 Modeling Domains, Users, and Interactions (pg. 525)
18.6 Natural Language and Naturalistic User Interfaces (pg. 531)
18.7 Human-Robot Ratio (pg. 538)
18.8 Trust (pg. 540)
18.9 Testing and Metrics (pg. 542)
18.10 Human-Robot Interaction and the Seven Areas of Artificial Intelligence (pg. 546)
18.11 Summary (pg. 547)
18.12 Exercises (pg. 549)
18.13 End Notes (pg. 552)
V. Design and the Ethics of Building Intelligent Robots (pg. 555)
19: Designing and Evaluating Autonomous Systems (pg. 557)
19.1 Overview (pg. 557)
19.2 Designing a Specific Autonomous Capability (pg. 559)
19.3 Case Study: Unmanned Ground Robotics Competition (pg. 562)
19.4 Taxonomies and Metrics versus System Design (pg. 569)
19.5 Holistic Evaluation of an Intelligent Robot (pg. 571)
19.6 Case Study: Concept Experimentation (pg. 578)
19.7 Summary (pg. 581)
19.8 Exercises (pg. 582)
20: Ethics (pg. 585)
20.1 Overview (pg. 585)
20.2 Types of Ethics (pg. 587)
20.3 Categorizations of Ethical Agents (pg. 588)
20.4 Programming Ethics (pg. 590)
20.5 Asimov's Three Laws of Robotics (pg. 591)
20.6 Artificial Intelligence and Implementing Ethics (pg. 593)
20.7 Summary (pg. 594)
20.8 Exercises (pg. 594)
20.9 End Notes (pg. 595)
Bibliography (pg. 597)
Index (pg. 613)

Robin R. Murphy

Robin R. Murphy is Raytheon Professor of Computer Science and Engineering at Texas A&M University, where she is also Director of the Center for Robot-Assisted Search and Rescue. She is the author of Introduction to AI Robotics and Disaster Robotics and the editor of Robotics Through Science Fiction (all published by the MIT Press).

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