328 Pages
100 B/W Illustrations
by
CRC Press
328 Pages
100 B/W Illustrations
by
CRC Press
Also available as eBook on:
Providing a guided tour of the pioneering work and major technical issues, Multiagent Robotic Systems addresses learning and adaptation in decentralized autonomous robots. Its systematic examination demonstrates the interrelationships between the autonomy of individual robots and the emerged global behavior properties of a group performing a cooperative task. The author also includes descriptions of the essential building blocks of the architecture of autonomous mobile robots with respect to their requirement on local behavioral conditioning and group behavioral evolution.
After reading this book you will be able to fully appreciate the strengths and usefulness of various approaches in the development and application of multiagent robotic systems. It covers:
Earlier works have focused primarily on how to navigate in a spatially unknown environment, given certain predefined motion behaviors. What is missing, however, is an in-depth look at the important issues on how to effectively obtain such behaviors in group robots and how to enable behavioral learning and adaptation at the group level. Multiagent Robotic Systems examines the key methodological issues and gives you an understanding of the underlying computational models and techniques for multiagent systems.
MOTIVATION, APPROACHES, AND OUTSTANDING ISSUES
Why Multiple Robots?
Advantages
Major Themes
Agents and Multiagent Systems
Multiagent Robots
Towards Cooperative Control
Cooperation Related Research
Learning, Evolution, and Adaptation
Design of Multi-Robot Control
Approaches
Behavior-Based Robotics
Collective Robotics
Evolutionary Robotics
Inspiration from Biology and Sociology
Summary
Models and Techniques
Reinforcement Learning
Genetic Algorithms
Artificial Life
Artificial Immune System
Probabilistic Modeling
Related Work on Multi-Robot Planning and Coordination
Outstanding Issues
Self-Organization
Local vs. Global Performance
Planning
Multi-Robot learning
Co-Evolution
Emergent Behavior
Reactive vs. Symbolic Systems
Heterogeneous vs. Homogenous Systems
Simulated vs. Physical Robots
Dynamics of Multiagent Robotic Systems
Summary
CASE STUDIES IN LEARNING
Multiagent Reinforcement Learning: Techniques
Autonomous Group Robots
Multiagent Reinforcement Learning
Summary
Multiagent Reinforcement Learning Results
Measurements
Group Behaviors
Multiagent Reinforcement Learning: What Matters
Collective Sensing
Initial Spatial Distribution
Inverted Sigmoid Function
Behavior Selection mechanism
Motion Mechanism
Emerging a Periodic Motion
Macro-Stable but Micro-Unstable Properties
Dominant Behavior
Evolutionary Multiagent Reinforcement Learning
Robot Group Example
Evolving Group Motion Strategies
Examples
Summary
CASE STUDIES IN ADAPTATION
Coordinated Maneuvers in a Dual-Agent System
Issues
Dual-Agent Learning
Specialized Roles in a Dual-Agent System
The Basic Capabilities of the Robot Agent
The Rationale of the Advice-Giving Agent
Acquiring Complex Maneuvers
Summary
Collective Behavior
Group Behavior
The Approach
Collective Box-Pushing by Applying Repulsive Forces
Collective Box-Pushing by Exerting External Contact Forces and Torques
Convergence Analysis for the Fittest-Preserved Evolution
Summary
CASE STUDIES IN SELF-ORGANIZATION
Multiagent Self-Organization
Artificial Potential Field
Overview of Self-Organization
Self-Organization of a Potential Map
Experiment 1
Experiment 2
Discussions
Evolutionary Multiagent Self-Organization
Evolution of Cooperative Motion Strategies
Experiments
Discussions
Summary
AN EXPLORATION TOOL
Toolboxes for Multiagent Robotics
Overview
Toolbox for Multiagent Reinforcement Learning
Toolbox for Evolutionary Multiagent Reinforcement Learning
Toolboxes for Evolutionary Collective Behavior Implementation
Toolbox for Multiagent Self-Organization
Toolbox for Evolutionary Multiagent Self-Organization
Example
INDEX
Why Multiple Robots?
Advantages
Major Themes
Agents and Multiagent Systems
Multiagent Robots
Towards Cooperative Control
Cooperation Related Research
Learning, Evolution, and Adaptation
Design of Multi-Robot Control
Approaches
Behavior-Based Robotics
Collective Robotics
Evolutionary Robotics
Inspiration from Biology and Sociology
Summary
Models and Techniques
Reinforcement Learning
Genetic Algorithms
Artificial Life
Artificial Immune System
Probabilistic Modeling
Related Work on Multi-Robot Planning and Coordination
Outstanding Issues
Self-Organization
Local vs. Global Performance
Planning
Multi-Robot learning
Co-Evolution
Emergent Behavior
Reactive vs. Symbolic Systems
Heterogeneous vs. Homogenous Systems
Simulated vs. Physical Robots
Dynamics of Multiagent Robotic Systems
Summary
CASE STUDIES IN LEARNING
Multiagent Reinforcement Learning: Techniques
Autonomous Group Robots
Multiagent Reinforcement Learning
Summary
Multiagent Reinforcement Learning Results
Measurements
Group Behaviors
Multiagent Reinforcement Learning: What Matters
Collective Sensing
Initial Spatial Distribution
Inverted Sigmoid Function
Behavior Selection mechanism
Motion Mechanism
Emerging a Periodic Motion
Macro-Stable but Micro-Unstable Properties
Dominant Behavior
Evolutionary Multiagent Reinforcement Learning
Robot Group Example
Evolving Group Motion Strategies
Examples
Summary
CASE STUDIES IN ADAPTATION
Coordinated Maneuvers in a Dual-Agent System
Issues
Dual-Agent Learning
Specialized Roles in a Dual-Agent System
The Basic Capabilities of the Robot Agent
The Rationale of the Advice-Giving Agent
Acquiring Complex Maneuvers
Summary
Collective Behavior
Group Behavior
The Approach
Collective Box-Pushing by Applying Repulsive Forces
Collective Box-Pushing by Exerting External Contact Forces and Torques
Convergence Analysis for the Fittest-Preserved Evolution
Summary
CASE STUDIES IN SELF-ORGANIZATION
Multiagent Self-Organization
Artificial Potential Field
Overview of Self-Organization
Self-Organization of a Potential Map
Experiment 1
Experiment 2
Discussions
Evolutionary Multiagent Self-Organization
Evolution of Cooperative Motion Strategies
Experiments
Discussions
Summary
AN EXPLORATION TOOL
Toolboxes for Multiagent Robotics
Overview
Toolbox for Multiagent Reinforcement Learning
Toolbox for Evolutionary Multiagent Reinforcement Learning
Toolboxes for Evolutionary Collective Behavior Implementation
Toolbox for Multiagent Self-Organization
Toolbox for Evolutionary Multiagent Self-Organization
Example
INDEX
Biography
Jiming Liu
"Liu and Wu describe the major developments and technical issues related to learning, adaptation, and self-organization in multiagent robotic systems … the list of references is comprehensive … A good resource for researchers on robotic systems, which may serve as a course resource for graduate students."
-CHOICE
