Cover image for Design and control of intelligent robotic systems
Title:
Design and control of intelligent robotic systems
Publication Information:
New York : Springer, 2009
Physical Description:
xxiii, 476 p. : ill. ; 25 cm.
ISBN:
9783540899327

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30000010201724 TJ211.35 D47 2009 Open Access Book Book
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Summary

Summary

With the increasing applications of intelligent robotic systems in various ?elds, the - sign and control of these systems have increasingly attracted interest from researchers. This edited book entitled "Design and Control of Intelligent Robotic Systems" in the book series of "Studies in Computational Intelligence" is a collection of some advanced research on design and control of intelligent robots. The works presented range in scope from design methodologies to robot development. Various design approaches and al- rithms, such as evolutionary computation, neural networks, fuzzy logic, learning, etc. are included. We also would like to mention that most studies reported in this book have been implemented in physical systems. An overview on the applications of computational intelligence in bio-inspired robotics is given in Chapter 1 by M. Begum and F. Karray, with highlights of the recent progress in bio-inspired robotics research and a focus on the usage of computational intelligence tools to design human-like cognitive abilities in the robotic systems. In Chapter 2, Lisa L. Grant and Ganesh K. Venayagamoorthy present greedy search, particle swarm optimization and fuzzy logic based strategies for navigating a swarm of robots for target search in a hazardous environment, with potential applications in high-risk tasks such as disaster recovery and hazardous material detection.


Table of Contents

From the contentsComputational Intelligence Techniques in Bio-inspired Robotics
Swarm Intelligence for Collective Robotic Search
A Glow worm Swarm Optimization based Multi-robot System for Signal Source Localization
Evolutionary Robotics: From Simulation-Based Behavior Learning to Direct Teaching in Real Environments
Ranked Pareto Particle Swarm Optimization for Mobile Robot Motion Planning
Path Planning Inspired on Emotional Intelligence
An Exploration of Online Parallel Learning in Heterogeneous Multi-Robot Swarms
Robot Control in Dynamic Environments using Memory-Based Learning
A Behavior based Control and Learning Approach to Real Robots
Incremental Acquisition of Neural Structures through Evolution
Evolutionary Terrain-Based Navigation of Autonomous Mobile Robots