Introduction to AI robotics

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1. Verfasser: Murphy, Robin 1957- (VerfasserIn)
Format: Buch
Sprache:English
Veröffentlicht: Cambridge, MA The MIT Press [2019]
Ausgabe:Second edition
Schriftenreihe:Intelligent robotics and autonomous agents
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Datensatz im Suchindex

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adam_text Contents I Framework for Thinking About AI and Robotics 1 What Are Intelligent Robots? 3 1 1 Overview 3 1 2 Definition: What Is an Intelligent Robot? 4 1 3 What Are the Components of a Robot? 7 1 4 Three Modalities: What Are the Kinds of Robots? 8 1 5 Motivation: Why Robots? 11 1 6 Seven Areas of AI: Why Intelligence? 13 1 7 Summary 15 1 8 Exercises 16 1 9 End Notes 17 2 A Brief History of AI Robotics 19 2 1 Overview 19 2 2 Robots as Tools, Agents, or Joint Cognitive Systems 20 2 3 World War II and the Nuclear Industry 21 2 4 Industrial Manipulators 24 2 5 Mobile Robots 29 2 6 Drones 35 2 7 The Move to Joint Cognitive Systems 36 2 8 Summary 37 2 9 Exercises 38 2 10 End Notes 38 3 Automation and Autonomy 41 3 1 Overview 41 3 2 The Four Sliders of Autonomous Capabilities 43 321 Plans: Generation versus Execution 44 322 Actions: Deterministic versus Non-deterministic 44 323 Models: Open- versus Closed-World 46 324 Knowledge Representation: Symbols versus Signals 3 3 Bounded Rationality 48 3 4 Impact of Automation and Autonomy 49 3 5 Impact on Programming Style 50 3 6 Impact on Hardware Design 50 3 7 Impact on Types of Functional Failures 52 371 Functional Failures 52 372 Impact on Types of Human Error 53 3 8 Trade-Spaces in Adding Autonomous Capabilities 55 3 9 Summary 57 3 10 Exercises 59 3 11 End Notes 61 Software Organization of Autonomy 63 4 1 Overview 64 4 2 The Three Types of Software Architectures 65 421 Types of Architectures 66 422 Architectures Reinforce Good Software Engineering Principles 67 4 3 Canonical A1 Robotics Operational Architecture 68 431 Attributes for Describing Layers 68 432 The Reactive Layer 70 433 The Deliberative Layer 71 434 The Interactive Layer 74 435 Canonical Operational Architecture Diagram 75 4 4 Other Operational Architectures 75 441 Levels of Automation 76 442 Autonomous Control Levels (ACL) 78 443 Levels of Initiative 80 4 5 Five Subsystems in Systems Architectures 82 4 6 Three Systems Architecture Paradigms 85 461 Trait 1: Interaction Between Primitives 85 462 Trait 2: Sensing Route 87 463 Hierarchical Systems Architecture Paradigm 89 464 Reactive Systems Paradigm 91 465 Hybrid Deliberative/Reactive Systems Paradigm 4 7 Execution Approval and Task Execution 95 4 8 Summary 97 4 9 Exercises 100 4 10 End Notes 101 Contents xi 5 Telesystems 103 5 1 Overview 104 5 2 Taskable Agency versus Remote Presence 105 5 3 The Seven Components of a Telesystem 105 5 4 Human Supervisory Control 108 541 Types of Supervisory Control 109 542 Human Supervisory Control for Telesystems 110 543 Manual Control 111 544 Traded Control 113 545 Shared Control 114 546 Guarded Motion 114 5 5 Human Factors 116 551 Cognitive Fatigue 117 552 Latency 118 553 Human: Robot Ratio 118 554 Human Out-of-the-Loop Control Problem 120 5 6 Guidelines for Determining if a Telesystem Is Suitable for an Application 122 561 Examples of Telesystems 123 5 7 Summary 125 5 8 Exercises 126 5 9 End Notes 128 II Reactive Functionality 129 6 Behaviors 131 6 1 Overview 131 6 2 Motivation for Exploring Animal Behaviors 132 6 3 Agency and Marr s Computational Theory 134 6 4 Example of Computational Theory: Rana Computatrix 137 6 5 Animal Behaviors 141 651 Reflexive Behaviors 142 6 6 Schema Theory 143 661 Schemas as Objects 143 662 Behaviors and Schema Theory 144 663 S-R: Schema Notation 146 6 7 Summary 148 6 8 Exercises 150 6 9 End Notes 151 7 Perception and Behaviors 153 7 1 Overview 153 7 2 Action-Perception Cycle 155 7 3 Gibson: Ecological Approach 731 Optic Flow 158 156 732 Nonvisual Affordances 159 7 4 Two Perceptual Systems 161 7 5 Innate Releasing Mechanisms 162 751 Definition of Innate Releasing Mechanisms 752 Concurrent Behaviors 170 7 6 Two Functions of Perception 171 7 7 Example: Cockroach Hiding 771 Decomposition 171 171 772 Identifying Releasers 172 773 Implicit versus Explicit Sequencing 176 774 Perception 177 775 Architectural Considerations 178 7 8 Summary 178 7 9 Exercises 181 7 10 End Notes 182 8 Behavioral Coordination 185 8 1 Overview 185 8 2 Coordination Function 186 8 3 Cooperating Methods: Potential Fields 188 831 Visualizing Potential Fields 188 832 Magnitude Profiles 191 833 Potential Fields and Perception 194 834 Programming a Single Potential Field 194 835 Combination of Fields and Behaviors 196 836 Example Using One Behavior per Sensor 199 837 Advantages and Disadvantages 202 8 4 Competing Methods: Subsumption 204 841 Example 206 8 5 Sequences: Finite State Automata 213 851A Follow the Road FSA 213 852A Pick Up the Trash FSA 217 8 6 Sequences: Scripts 220 8 7 AI and Behavior Coordination 222 8 8 Summary 223 8 9 Exercises 224 8 10 End Notes 226 Contents xiii 9 Locomotion 229 9 1 Overview 229 9 2 Mechanical Locomotion 230 921 Holonomic versus Nonholonomic 231 922 Steering 231 9 3 Biomimetic Locomotion 235 9 4 Legged Locomotion 238 941 Number of Leg Events 239 942 Balance 240 943 Gaits 243 944 Legs with Joints 243 9 5 Action Selection 245 9 6 Summary 246 9 7 Exercises 247 9 8 End Notes 249 10 Sensors and Sensing 251 10 1 Overview 252 10 2 Sensor and Sensing Model 253 10 2 1 Sensors: Active or Passive 254 10 2 2 Sensors: Types of Output and Usage 255 10 3 Odometry, Inertial Navigation System (INS) and Global Positioning System (GPS) 255 10 4 Proximity Sensors 256 10 5 Computer Vision 258 10 5 1 Computer Vision Definition 258 10 5 2 Grayscale and Color Representation 259 10 5 3 Region Segmentation 264 10 5 4 Color Histogramming 267 10 6 Choosing Sensors and Sensing 269 10 6 1 Logical Sensors 269 10 6 2 Behavioral Sensor Fusion 271 10 6 3 Designing a Sensor Suite 274 10 7 Summary 278 10 8 Exercises 280 10 9 End Notes 283 11 Range Sensing 285 11 1 Overview 285 11 2 Stereo 288 11 3 Depth from X 293 11 4 Sonar or Ultrasonics 293 11 4 1 Light Stripers 300 11 4 2 Lidar 302 11 4 3 RGB-D Cameras 304 11 4 4 Point Clouds 304 11 5 Case Study: Hors d Oeuvres, Anyone! 11 6 Summary 315 11 7 Exercises 315 11 8 End Notes 317 Ill Deliberative Functionality 319 12 Deliberation 321 12 1 Overview 321 12 2 Strips 323 12 2 1 More Realistic Strips Example 326 12 2 2 Strips Summary 331 12 2 3 Revisiting the Closed-World Assumption and the Frame Problem 332 12 3 Symbol Grounding Problem 333 12 4 Global World Models 335 12 4 1 Local Perceptual Spaces 335 12 4 2 Multi-level or Hierarchical World Models 336 12 4 3 Virtual Sensors 338 12 4 4 Global World Model and Deliberation 339 12 5 Nested Hierarchical Controller 339 12 6 RAPS and 3T 342 12 7 Fault Detection Identification and Recovery 346 12 8 Programming Considerations 347 12 9 Summary 348 12 10 Exercises 349 12 11 End Notes 351 13 Navigation 353 13 1 Overview 353 13 2 The Four Questions of Navigation 355 13 3 Spatial Memory 358 13 4 Types of Path Planning 359 13 5 Landmarks and Gateways 361 13 6 Relational Methods 364 13 6 1 Distinctive Places 365 13 6 2 Advantages and Disadvantages 368 13 7 Associative Methods 369 Contents 13 8 Case Study of Topological Navigation with a Hybrid Architecture 13 8 1 Topological Path Planning 370 13 8 2 Navigation Scripts 375 13 8 3 Lessons Learned 378 13 9 Discussion of Opportunities for AI 379 13 10 Summary 381 13 11 Exercises 382 13 12 End Notes 384 14 Metric Path Planning and Motion Planning 385 14 1 Overview 385 14 2 Four Situations Where Topological Navigation Is Not Sufficient 14 3 Configuration Space 389 14 3 1 Meadow Maps 391 14 3 2 Generalized Voronoi Graphs 393 14 3 3 Regular Grids 394 14 3 4 Quadtrees 395 14 4 Metric Path Planning 396 14 4 1 A* and Graph-Based Planners 396 14 4 2 Wavefront-Based Planners 402 14 5 Executing a Planned Path 402 14 5 1 Subgoal Obsession 402 14 5 2 Replanning 404 14 6 Motion Planning 407 14 7 Criteria for Evaluating Path and Motion Planners 410 14 8 Summary 411 14 9 Exercises 413 14 10 End Notes 415 15 Localization, Mapping, and Exploration 417 15 1 Overview 418 15 2 Localization 419 15 3 Feature-Based Localization 421 15 4 Iconic Localization 423 15 5 Static versus Dynamic Environments 424 15 6 Simultaneous Localization and Mapping 424 15 7 Terrain Identification and Mapping 426 15 7 1 Digital Terrain Elevation Maps 427 15 7 2 Terrain Identification 427 15 7 3 Stereophotogrammetry 428 15 8 Scale and Traversability 432 15 8 1 Scale 432 15 8 2 Traversability Attributes 434 XVI Contents 15 9 Exploration 435 15 9 1 Reactive Exploration 435 15 9 2 Frontier-Based Exploration 436 15 9 3 Generalized Voronoi Graph Methods 437 15 10 Localization, Mapping, Exploration, and AI 439 15 11 Summary 441 15 12 Exercises 442 15 13 End Notes 443 16 Learning 445 16 1 Overview 446 16 2 Learning 447 16 3 Types of Learning by Example 449 16 4 Common Supervised Learning Algorithms 450 16 4 1 Induction 450 16 4 2 Support Vector Machines 452 16 4 3 Decision Trees 452 16 5 Common Unsupervised Learning Algorithms 454 16 5 1 Clustering 454 16 5 2 Artificial Neural Networks 455 16 6 Reinforcement Learning 460 16 6 1 Utility Functions 461 16 6 2 Q-leaming 461 16 6 3 Q-leaming Example 464 16 6 4 Q-leaming Discussion 468 16 7 Evolutionary Robotics and Genetic Algorithms 468 16 8 Learning and Architecture 473 16 9 Gaps and Opportunities 474 16 10 Summary 475 16 11 Exercises 476 16 12 End Notes 478 IV Interactive Functionality 481 17 MultiRobot Systems (MRS) 483 17 1 Overview 484 17 2 Four Opportunities and Seven Challenges 484 17 2 1 Four Advantages of MRS 485 17 2 2 Seven Challenges in MRS 486 17 3 Multirobot Systems and A1 487 17 4 Designing MRS for Tasks 490 17 4 1 Tune Expectations for a Task 490 Contents xvii 17 4 2 Subject of Action 491 17 4 3 Movement 492 17 4 4 Dependency 492 17 5 Coordination Dimension of MRS Design 493 17 6 Systems Dimensions in Design 494 17 6 1 Communication 495 17 6 2 MRS Composition 496 17 6 3 Team Size 498 17 7 Five Most Common Occurrences of MRS 499 17 8 Operational Architectures for MRS 501 17 9 Task Allocation 503 17 10 Summary 504 17 11 Exercises 505 17 12 End Notes 508 18 Human-Robot Interaction 511 18 1 Overview 512 18 2 Taxonomy of Interaction 514 18 3 Contributions from HCI, Psychology, Communications 516 18 3 1 Human-Computer Interaction 516 18 3 2 Psychology 517 18 3 3 Communications 518 18 4 User Interfaces 518 18 4 1 Eight Golden Rules for User Interface Design 519 18 4 2 Situation Awareness 522 18 4 3 Multiple Users 525 18 5 Modeling Domains, Users, and Interactions 525 18 5 1 Motivating Example of Users and Interactions 526 18 5 2 Cognitive Task Analysis 528 18 5 3 Cognitive Work Analysis 529 18 6 Natural Language and Naturalistic User Interfaces 531 18 6 1 Natural Language Understanding 531 18 6 2 Semantics and Communication 533 18 6 3 Models of the Inner State of the Agent 534 18 6 4 Multi-modal Communication 535 18 7 Human-Robot Ratio 538 18 8 Trust 540 18 9 Testing and Metrics 542 18 9 1 Data Collection Methods 543 18 9 2 Metrics 545 18 10 Human-Robot Interaction and the Seven Areas of Artificial Intelligence 546 18 11 Summary 547 18 12 Exercises 549 18 13 End Notes 552 V Design and the Ethics of Building Intelligent Robots 555 19 Designing and Evaluating Autonomous Systems 557 19 1 Overview 557 19 2 Designing a Specific Autonomous Capability 559 19 2 1 Design Philosophy 559 19 2 2 Five Questions for Designing an Autonomous Robot 19 3 Case Study: Unmanned Ground Robotics Competition 562 19 4 Taxonomies and Metrics versus System Design 569 19 5 Holistic Evaluation of an Intelligent Robot 571 19 5 1 Failure Taxonomy 572 19 5 2 Four Types of Experiments 573 19 5 3 Data to Collect 575 19 6 Case Study: Concept Experimentation 578 19 7 Summary 581 19 8 Exercises 582 19 9 End Notes 583 20 Ethics 585 20 1 Overview 585 20 2 Types of Ethics 587 20 3 Categorizations of Ethical Agents 588 20 3 1 Moor s Four Categories 588 20 3 2 Categories of Morality 589 20 4 Programming Ethics 590 20 4 1 Approaches from Philosophy 590 20 4 2 Approaches from Robotics 591 20 5 Asimov s Three Laws of Robotics 591 20 5 1 Problems with the Three Laws 592 20 5 2 The Three Laws of Responsible Robotics 592 20 6 Artificial Intelligence and Implementing Ethics 593 20 7 Summary 594 20 8 Exercises 594 20 9 End Notes 595 Bibliography 597
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spellingShingle Murphy, Robin 1957-
Introduction to AI robotics
Robotics
Artificial intelligence
Roboter (DE-588)4050208-9 gnd
Künstliche Intelligenz (DE-588)4033447-8 gnd
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title Introduction to AI robotics
title_auth Introduction to AI robotics
title_exact_search Introduction to AI robotics
title_full Introduction to AI robotics Robin R. Murphy
title_fullStr Introduction to AI robotics Robin R. Murphy
title_full_unstemmed Introduction to AI robotics Robin R. Murphy
title_short Introduction to AI robotics
title_sort introduction to ai robotics
topic Robotics
Artificial intelligence
Roboter (DE-588)4050208-9 gnd
Künstliche Intelligenz (DE-588)4033447-8 gnd
Robotik (DE-588)4261462-4 gnd
Maschinelles Lernen (DE-588)4193754-5 gnd
topic_facet Robotics
Artificial intelligence
Roboter
Künstliche Intelligenz
Robotik
Maschinelles Lernen
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