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E-raamat: Introduction to AI Robotics, second edition

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(Texas A&M University)
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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.



A comprehensive survey of artificial intelligence algorithms and programming organization for robot systems, combining theoretical rigor and practical applications.
I Framework for Thinking About AI and Robotics 1(128)
1 What Are Intelligent Robots?
3(16)
1.1 Overview
3(1)
1.2 Definition: What Is an Intelligent Robot?
4(3)
1.3 What Are the Components of a Robot?
7(1)
1.4 Three Modalities: What Are the Kinds of Robots?
8(3)
1.5 Motivation: Why Robots?
11(2)
1.6 Seven Areas of AI: Why Intelligence?
13(2)
1.7 Summary
15(1)
1.8 Exercises
16(1)
1.9 End Notes
17(2)
2 A Brief History of AI Robotics
19(22)
2.1 Overview
19(1)
2.2 Robots as Tools, Agents, or Joint Cognitive Systems
20(1)
2.3 World War II and the Nuclear Industry
21(3)
2.4 Industrial Manipulators
24(5)
2.5 Mobile Robots
29(6)
2.6 Drones
35(1)
2.7 The Move to Joint Cognitive Systems
36(1)
2.8 Summary
37(1)
2.9 Exercises
38(1)
2.10 End Notes
38(3)
3 Automation and Autonomy
41(22)
3.1 Overview
41(2)
3.2 The Four Sliders of Autonomous Capabilities
43(5)
3.2.1 Plans: Generation versus Execution
44(1)
3.2.2 Actions: Deterministic versus Non-deterministic
44(2)
3.2.3 Models: Open- versus Closed-World
46(2)
3.2.4 Knowledge Representation: Symbols versus Signals
48(1)
3.3 Bounded Rationality
48(1)
3.4 Impact of Automation and Autonomy
49(1)
3.5 Impact on Programming Style
50(1)
3.6 Impact on Hardware Design
50(2)
3.7 Impact on Types of Functional Failures
52(3)
3.7.1 Functional Failures
52(1)
3.7.2 Impact on Types of Human Error
53(2)
3.8 Trade-Spaces in Adding Autonomous Capabilities
55(2)
3.9 Summary
57(2)
3.10 Exercises
59(2)
3.11 End Notes
61(2)
4 Software Organization of Autonomy
63(40)
4.1 Overview
64(1)
4.2 The Three Types of Software Architectures
65(3)
4.2.1 Types of Architectures
66(1)
4.2.2 Architectures Reinforce Good Software Engineering Principles
67(1)
4.3 Canonical AI Robotics Operational Architecture
68(7)
4.3.1 Attributes for Describing Layers
68(2)
4.3.2 The Reactive Layer
70(1)
4.3.3 The Deliberative Layer
71(3)
4.3.4 The Interactive Layer
74(1)
4.3.5 Canonical Operational Architecture Diagram
75(1)
4.4 Other Operational Architectures
75(7)
4.4.1 Levels of Automation
76(2)
4.4.2 Autonomous Control Levels (ACL)
78(2)
4.4.3 Levels of Initiative
80(2)
4.5 Five Subsystems in Systems Architectures
82(3)
4.6 Three Systems Architecture Paradigms
85(10)
4.6.1 Trait 1: Interaction Between Primitives
85(2)
4.6.2 Trait 2: Sensing Route
87(2)
4.6.3 Hierarchical Systems Architecture Paradigm
89(2)
4.6.4 Reactive Systems Paradigm
91(2)
4.6.5 Hybrid Deliberative/Reactive Systems Paradigm
93(2)
4.7 Execution Approval and Task Execution
95(2)
4.8 Summary
97(3)
4.9 Exercises
100(1)
4.10 End Notes
101(2)
5 Telesystems
103(26)
5.1 Overview
104(1)
5.2 Taskable Agency versus Remote Presence
105(1)
5.3 The Seven Components of a Telesystem
105(3)
5.4 Human Supervisory Control
108(8)
5.4.1 Types of Supervisory Control
109(1)
5.4.2 Human Supervisory Control for Telesystems
110(1)
5.4.3 Manual Control
111(2)
5.4.4 Traded Control
113(1)
5.4.5 Shared Control
114(1)
5.4.6 Guarded Motion
114(2)
5.5 Human Factors
116(6)
5.5.1 Cognitive Fatigue
117(1)
5.5.2 Latency
118(1)
5.5.3 Human: Robot Ratio
118(2)
5.5.4 Human Out-of-the-Loop Control Problem
120(2)
5.6 Guidelines for Determining if a Telesystem Is Suitable for an Application
122(3)
5.6.1 Examples of Telesystems
123(2)
5.7 Summary
125(1)
5.8 Exercises
126(2)
5.9 End Notes
128(1)
II Reactive Functionality 129(190)
6 Behaviors
131(22)
6.1 Overview
131(1)
6.2 Motivation for Exploring Animal Behaviors
132(2)
6.3 Agency and Marr's Computational Theory
134(3)
6.4 Example of Computational Theory: Rana Computatrix
137(4)
6.5 Animal Behaviors
141(2)
6.5.1 Reflexive Behaviors
142(1)
6.6 Schema Theory
143(5)
6.6.1 Schemas as Objects
143(1)
6.6.2 Behaviors and Schema Theory
144(2)
6.6.3 S-R: Schema Notation
146(2)
6.7 Summary
148(2)
6.8 Exercises
150(1)
6.9 End Notes
151(2)
7 Perception and Behaviors
153(32)
7.1 Overview
153(2)
7.2 Action-Perception Cycle
155(1)
7.3 Gibson: Ecological Approach
156(5)
7.3.1 Optic Flow
158(1)
7.3.2 Nonvisual Affordances
159(2)
7.4 Two Perceptual Systems
161(1)
7.5 Innate Releasing Mechanisms
162(9)
7.5.1 Definition of Innate Releasing Mechanisms
165(5)
7.5.2 Concurrent Behaviors
170(1)
7.6 Two Functions of Perception
171(1)
7.7 Example: Cockroach Hiding
171(7)
7.7.1 Decomposition
171(1)
7.7.2 Identifying Releasers
172(4)
7.7.3 Implicit versus Explicit Sequencing
176(1)
7.7.4 Perception
177(1)
7.7.5 Architectural Considerations
178(1)
7.8 Summary
178(3)
7.9 Exercises
181(1)
7.10 End Notes
182(3)
8 Behavioral Coordination
185(44)
8.1 Overview
185(1)
8.2 Coordination Function
186(2)
8.3 Cooperating Methods: Potential Fields
188(16)
8.3.1 Visualizing Potential Fields
188(3)
8.3.2 Magnitude Profiles
191(3)
8.3.3 Potential Fields and Perception
194(1)
8.3.4 Programming a Single Potential Field
194(2)
8.3.5 Combination of Fields and Behaviors
196(3)
8.3.6 Example Using One Behavior per Sensor
199(3)
8.3.7 Advantages and Disadvantages
202(2)
8.4 Competing Methods: Subsumption
204(9)
8.4.1 Example
206(7)
8.5 Sequences: Finite State Automata
213(7)
8.5.1 A Follow the Road FSA
213(4)
8.5.2 A Pick Up the Trash FSA
217(3)
8.6 Sequences: Scripts
220(2)
8.7 AI and Behavior Coordination
222(1)
8.8 Summary
223(1)
8.9 Exercises
224(2)
8.10 End Notes
226(3)
9 Locomotion
229(22)
9.1 Overview
229(1)
9.2 Mechanical Locomotion
230(5)
9.2.1 Holonomic versus Nonholonomic
231(1)
9.2.2 Steering
231(4)
9.3 Biomimetic Locomotion
235(3)
9.4 Legged Locomotion
238(7)
9.4.1 Number of Leg Events
239(1)
9.4.2 Balance
240(3)
9.4.3 Gaits
243(1)
9.4.4 Legs with Joints
243(2)
9.5 Action Selection
245(1)
9.6 Summary
246(1)
9.7 Exercises
247(2)
9.8 End Notes
249(2)
10 Sensors and Sensing
251(34)
10.1 Overview
252(1)
10.2 Sensor and Sensing Model
253(2)
10.2.1 Sensors: Active or Passive
254(1)
10.2.2 Sensors: Types of Output and Usage
255(1)
10.3 Odometry, Inertial Navigation System (INS) and Global Positioning System (GPS)
255(1)
10.4 Proximity Sensors
256(2)
10.5 Computer Vision
258(11)
10.5.1 Computer Vision Definition
258(1)
10.5.2 Grayscale and Color Representation
259(5)
10.5.3 Region Segmentation
264(3)
10.5.4 Color Histogramming
267(2)
10.6 Choosing Sensors and Sensing
269(9)
10.6.1 Logical Sensors
269(2)
10.6.2 Behavioral Sensor Fusion
271(3)
10.6.3 Designing a Sensor Suite
274(4)
10.7 Summary
278(2)
10.8 Exercises
280(3)
10.9 End Notes
283(2)
11 Range Sensing
285(34)
11.1 Overview
285(3)
11.2 Stereo
288(5)
11.3 Depth from X
293(1)
11.4 Sonar or Ultrasonics
293(14)
11.4.1 Light Stripers
300(2)
11.4.2 Lidar
302(2)
11.4.3 RGB-D Cameras
304(1)
11.4.4 Point Clouds
304(3)
11.5 Case Study: Hors d'Oeuvres, Anyone?
307(8)
11.6 Summary
315(1)
11.7 Exercises
315(2)
11.8 End Notes
317(2)
III Deliberative Functionality 319(162)
12 Deliberation
321(32)
12.1 Overview
321(2)
12.2 Strips
323(12)
12.2.1 More Realistic Strips Example
326(5)
12.2.2 Strips Summary
331(1)
12.2.3 Revisiting the Closed-World Assumption and the Frame Problem
332(1)
12.3 Symbol Grounding Problem
333(2)
12.4 Global World Models
335(4)
12.4.1 Local Perceptual Spaces
335(1)
12.4.2 Multi-level or Hierarchical World Models
336(2)
12.4.3 Virtual Sensors
338(1)
12.4.4 Global World Model and Deliberation
339(1)
12.5 Nested Hierarchical Controller
339(3)
12.6 RAPS and 3T
342(4)
12.7 Fault Detection Identification and Recovery
346(1)
12.8 Programming Considerations
347(1)
12.9 Summary
348(1)
12.10 Exercises
349(2)
12.11 End Notes
351(2)
13 Navigation
353(32)
13.1 Overview
353(2)
13.2 The Four Questions of Navigation
355(3)
13.3 Spatial Memory
358(1)
13.4 Types of Path Planning
359(2)
13.5 Landmarks and Gateways
361(3)
13.6 Relational Methods
364(5)
13.6.1 Distinctive Places
365(3)
13.6.2 Advantages and Disadvantages
368(1)
13.7 Associative Methods
369(1)
13.8 Case Study of Topological Navigation with a Hybrid Architecture
369(10)
13.8.1 Topological Path Planning
370(5)
13.8.2 Navigation Scripts
375(3)
13.8.3 Lessons Learned
378(1)
13.9 Discussion of Opportunities for AI
379(2)
13.10 Summary
381(1)
13.11 Exercises
382(2)
13.12 End Notes
384(1)
14 Metric Path Planning and Motion Planning
385(32)
14.1 Overview
385(2)
14.2 Four Situations Where Topological Navigation Is Not Sufficient
387(2)
14.3 Configuration Space
389(7)
14.3.1 Meadow Maps
391(2)
14.3.2 Generalized Voronoi Graphs
393(1)
14.3.3 Regular Grids
394(1)
14.3.4 Quadtrees
395(1)
14.4 Metric Path Planning
396(6)
14.4.1 A and Graph-Based Planners
396(6)
14.4.2 Wavefront-Based Planners
402(1)
14.5 Executing a Planned Path
402(5)
14.5.1 Subgoal Obsession
402(2)
14.5.2 Replanning
404(3)
14.6 Motion Planning
407(3)
14.7 Criteria for Evaluating Path and Motion Planners
410(1)
14.8 Summary
411(2)
14.9 Exercises
413(2)
14.10 End Notes
415(2)
15 Localization, Mapping, and Exploration
417(28)
15.1 Overview
418(1)
15.2 Localization
419(2)
15.3 Feature-Based Localization
421(2)
15.4 Iconic Localization
423(1)
15.5 Static versus Dynamic Environments
424(1)
15.6 Simultaneous Localization and Mapping
424(2)
15.7 Terrain Identification and Mapping
426(6)
15.7.1 Digital Terrain Elevation Maps
427(1)
15.7.2 Terrain Identification
427(1)
15.7.3 Stereophotogrammetry
428(4)
15.8 Scale and Traversability
432(3)
15.8.1 Scale
432(2)
15.8.2 Traversability Attributes
434(1)
15.9 Exploration
435(4)
15.9.1 Reactive Exploration
435(1)
15.9.2 Frontier-Based Exploration
436(1)
15.9.3 Generalized Voronoi Graph Methods
437(2)
15.10 Localization, Mapping, Exploration, and AI
439(2)
15.11 Summary
441(1)
15.12 Exercises
442(1)
15.13 End Notes
443(2)
16 Learning
445(36)
16.1 Overview
446(1)
16.2 Learning
447(2)
16.3 Types of Learning by Example
449(1)
16.4 Common Supervised Learning Algorithms
450(4)
16.4.1 Induction
450(2)
16.4.2 Support Vector Machines
452(1)
16.4.3 Decision Trees
452(2)
16.5 Common Unsupervised Learning Algorithms
454(6)
16.5.1 Clustering
454(1)
16.5.2 Artificial Neural Networks
455(5)
16.6 Reinforcement Learning
460(8)
16.6.1 Utility Functions
461(1)
16.6.2 Q-learning
461(3)
16.6.3 Q-learning Example
464(4)
16.6.4 Q-learning Discussion
468(1)
16.7 Evolutionary Robotics and Genetic Algorithms
468(5)
16.8 Learning and Architecture
473(1)
16.9 Gaps and Opportunities
474(1)
16.10 Summary
475(1)
16.11 Exercises
476(2)
16.12 End Notes
478(3)
IV Interactive Functionality 481(74)
17 MultiRobot Systems (MRS)
483(28)
17.1 Overview
484(1)
17.2 Four Opportunities and Seven Challenges
484(3)
17.2.1 Four Advantages of MRS
485(1)
17.2.2 Seven Challenges in MRS
486(1)
17.3 Multirobot Systems and AI
487(3)
17.4 Designing MRS for Tasks
490(3)
17.4.1 Time Expectations for a Task
490(1)
17.4.2 Subject of Action
491(1)
17.4.3 Movement
492(1)
17.4.4 Dependency
492(1)
17.5 Coordination Dimension of MRS Design
493(1)
17.6 Systems Dimensions in Design
494(5)
17.6.1 Communication
495(1)
17.6.2 MRS Composition
496(2)
17.6.3 Team Size
498(1)
17.7 Five Most Common Occurrences of MRS
499(2)
17.8 Operational Architectures for MRS
501(2)
17.9 Task Allocation
503(1)
17.10 Summary
504(1)
17.11 Exercises
505(3)
17.12 End Notes
508(3)
18 Human-Robot Interaction
511(44)
18.1 Overview
512(2)
18.2 Taxonomy of Interaction
514(2)
18.3 Contributions from HCI, Psychology, Communications
516(2)
18.3.1 Human-Computer Interaction
516(1)
18.3.2 Psychology
517(1)
18.3.3 Communications
518(1)
18.4 User Interfaces
518(7)
18.4.1 Eight Golden Rules for User Interface Design
519(3)
18.4.2 Situation Awareness
522(3)
18.4.3 Multiple Users
525(1)
18.5 Modeling Domains, Users, and Interactions
525(6)
18.5.1 Motivating Example of Users and Interactions
526(2)
18.5.2 Cognitive Task Analysis
528(1)
18.5.3 Cognitive Work Analysis
529(2)
18.6 Natural Language and Naturalistic User Interfaces
531(7)
18.6.1 Natural Language Understanding
531(2)
18.6.2 Semantics and Communication
533(1)
18.6.3 Models of the Inner State of the Agent
534(1)
18.6.4 Multi-modal Communication
535(3)
18.7 Human-Robot Ratio
538(2)
18.8 Trust
540(2)
18.9 Testing and Metrics
542(4)
18.9.1 Data Collection Methods
543(2)
18.9.2 Metrics
545(1)
18.10 Human-Robot Interaction and the Seven Areas of Artificial Intelligence
546(1)
18.11 Summary
547(2)
18.12 Exercises
549(3)
18.13 End Notes
552(3)
V Design and the Ethics of Building Intelligent Robots 555(42)
19 Designing and Evaluating Autonomous Systems
557(28)
19.1 Overview
557(2)
19.2 Designing a Specific Autonomous Capability
559(3)
19.2.1 Design Philosophy
559(1)
19.2.2 Five Questions for Designing an Autonomous Robot
560(2)
19.3 Case Study: Unmanned Ground Robotics Competition
562(7)
19.4 Taxonomies and Metrics versus System Design
569(2)
19.5 Holistic Evaluation of an Intelligent Robot
571(7)
19.5.1 Failure Taxonomy
572(1)
19.5.2 Four Types of Experiments
573(2)
19.5.3 Data to Collect
575(3)
19.6 Case Study: Concept Experimentation
578(3)
19.7 Summary
581(1)
19.8 Exercises
582(1)
19.9 End Notes
583(2)
20 Ethics
585(12)
20.1 Overview
585(2)
20.2 Types of Ethics
587(1)
20.3 Categorizations of Ethical Agents
588(2)
20.3.1 Moor's Four Categories
588(1)
20.3.2 Categories of Morality
589(1)
20.4 Programming Ethics
590(1)
20.4.1 Approaches from Philosophy
590(1)
20.4.2 Approaches from Robotics
591(1)
20.5 Asimov's Three Laws of Robotics
591(2)
20.5.1 Problems with the Three Laws
592(1)
20.5.2 The Three Laws of Responsible Robotics
592(1)
20.6 Artificial Intelligence and Implementing Ethics
593(1)
20.7 Summary
594(1)
20.8 Exercises
594(1)
20.9 End Notes
595(2)
Bibliography 597(16)
Index 613
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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