Design and Control of Intelligent Robotic Systems

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

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.

• Autonomous Robotics
• Autonomous Robotics Systems
• Computational Intelligence
• Motion Planning
• Navigation
• algorithms
• autonom
• fuzzy logic
• mobile robot
• robot
• robotics

• 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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