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Design and Control of Intelligent Robotic Systems

  • Book
  • © 2009

Overview

Editors:
  1. Dikai Liu

    1. University of Technology, Sydney, Australia

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  2. Lingfeng Wang

    1. Texas A&M University, College Station, USA

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  3. Kay Chen Tan

    1. National University of Singapore, Singapore

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  • Presents current applications of computational intelligence in autonomous robotic systems

Part of the book series: Studies in Computational Intelligence (SCI, volume 177)

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Table of contents (21 chapters)

  1. Front Matter

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  2. Computational Intelligence Techniques in Bio-inspired Robotics

    • M. Begum, F. Karray
    Pages 1-28

  3. Swarm Intelligence for Collective Robotic Search

    • Lisa L. Grant, Ganesh K. Venayagamoorthy
    Pages 29-47

  4. A Glowworm Swarm Optimization Based Multi-robot System for Signal Source Localization

    • K. N. Krishnanand, D. Ghose
    Pages 49-68

  5. Evolutionary Robotics: From Simulation-Based Behavior Learning to Direct Teaching in Real Environments

    • Seiji Yamada, Daisuke Katagami
    Pages 69-96

  6. Ranked Pareto Particle Swarm Optimization for Mobile Robot Motion Planning

    • D. Wang, N. M. Kwok, D. K. Liu, Q. P. Ha
    Pages 97-118

  7. Path Planning Inspired on Emotional Intelligence

    • R. Barber, V. Egido, M. Malfaz, M. A. Salichs
    Pages 119-132

  8. An Exploration of Online Parallel Learning in Heterogeneous Multi-robot Swarms

    • Jim Pugh, Alcherio Martinoli
    Pages 133-151

  9. Robot Control in Dynamic Environments Using Memory-Based Learning

    • Patrick M. McDowell, Brian S. Bourgeois, Frederick E. Petry
    Pages 153-170

  10. A Behavior Based Control and Learning Approach to Real Robots

    • Dongbing Gu, Jindong Liu, Huosheng Hu
    Pages 171-186

  11. Incremental Acquisition of Neural Structures through Evolution

    • Yohannes Kassahun, Jan Hendrik Metzen, Mark Edgington, Frank Kirchner
    Pages 187-208

  12. Evolutionary Terrain-Based Navigation of Autonomous Mobile Robots

    • Terrence P. Fries
    Pages 209-226

  13. A Novel Intelligent Control Approach for Precise Tracking of Autonomous Robots

    • Sukumar Kamalasadan, Adel A. Ghandakly
    Pages 227-254

  14. Punctuated Anytime Learning to Evolve Robot Control for Area Coverage

    • Gary B. Parker
    Pages 255-277

  15. Local Methods for Supporting Grounded Communication in Robot Teams

    • Nathan Wiebe, John Anderson
    Pages 279-301

  16. Vision SLAM Maps: Towards Richer Content

    • Daniel C. Asmar, Samer M. Abdallah, John S. Zelek
    Pages 303-329

  17. Learning of Hierarchical Perception-Action Cycles

    • Florian Hoppe, Gerald Sommer
    Pages 331-353

  18. Advancing Artificial Intelligence through Minimalist Humanoid Robotics

    • Jacky Baltes, John Anderson
    Pages 355-376

  19. Design, Modeling and Control of an Ankle Rehabilitation Robot

    • Yun-Ho Tsoi, Shane Q. Xie, Andrew E. Graham
    Pages 377-399

  20. Markovito: A Flexible and General Service Robot

    • Héctor Hugo Avilés-Arriaga, L. Enrique Sucar, Eduardo F. Morales, Blanca A. Vargas, Josué Sánchez, Elva Corona
    Pages 401-423
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Keywords

About this book

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.

Editors and Affiliations

  • University of Technology, Sydney, Australia

    Dikai Liu

  • Texas A&M University, College Station, USA

    Lingfeng Wang

  • National University of Singapore, Singapore

    Kay Chen Tan

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