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。

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,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