1st Edition

Multiagent Robotic Systems




ISBN 9780849322884
Published May 30, 2001 by CRC Press
328 Pages - 100 B/W Illustrations

USD $195.00

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Book Description

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:

  • Why and how to develop and experimentally test the computational mechanisms for learning and evolving sensory-motor control behaviors in autonomous robots
  • How to design and develop evolutionary algorithm-based group behavioral learning mechanisms for the optimal emergence of group behaviors
  • How to enable group robots to converge to a finite number of desirable task states through group learning
  • What are the effects of the local learning mechanisms on the emergent global behaviors
  • How to use decentralized, self-organizing autonomous robots to perform cooperative tasks in an unknown environment
    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.
  • Table of Contents

    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

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    Reviews

    "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