Multiagent Robotic Systems: 1st Edition (Hardback) book cover

Multiagent Robotic Systems

1st Edition

By Jiming Liu, Jianbing Wu

CRC Press

328 pages | 100 B/W Illus.

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Hardback: 9780849322884
pub: 2001-05-30
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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.

  • 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

    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

    About the Series

    International Series on Computational Intelligence

    Learn more…

    Subject Categories

    BISAC Subject Codes/Headings:
    TEC007000
    TECHNOLOGY & ENGINEERING / Electrical