Liu / Wu Multiagent Robotic Systems

Erscheinungsjahr 2010
ISBN: 978-1-4200-3883-5
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 328 Seiten

Reihe: International Series on Computational Intelligence

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.

Zielgruppe

Underraduate and graduate students in computer science and most engineering disciplines; computer scientists, engineers, researchers, and practitioners in the field of machine intelligence

Autoren/Hrsg.

Liu, Jiming

Wu, Jianbing

Fachgebiete

• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Überwachungstechnik

Inhaltsverzeichnis

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