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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
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