
Introduction to AI Robotics, second edition
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Content
- Intro
- Series Page
- Title Page
- Copyright
- Dedication
- Brief Contents
- Table of Contents
- Preface
- Acknowledgments
- 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. Global World Models
- 12.4.1. Local Perceptual Spaces
- 12.4.2. Multi-level or Hierarchical World 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. Cognitive Work 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
- 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
- Series List
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