Soft Computing for Intelligent Robotic Systems

Research results using some of the most advanced soft computing techniques in intelligent robotic systems are presented. The main purpose of this book is to show how the power of soft computing techniques can be exploited in intelligent robotic systems. The main emphasis is on control system for a mobile robot, behavior arbitration for a mobile robot, reinforcement learning of a robot, manipulation of a robot, collision avoidance and automatic design of robots.
This book will be useful for application engineers, scientists and researchers who wish to use some of the most advanced soft computing techniques in robotics.

Contenu

L.C. Jain, T. Fukuda: Preface.- K. Watanabe, K. Izumi: A fuzzy-neural realization of behavior-based control systems for a mobile robot.- A. Ishiguro, Y. Watanabe, T. Kondo, Y. Uchikawa: Artificial immune network and its application to robotics.- A. Johannet, I. Sarda: Reinforcement learning of a six-legged robot to walk and avoid obstacles.- J.P. Urban, J.L. Buessler, J. Gresser: Neural networks for visual servoing in robotics.- Y.H. Kim, F.L. Lewis: Intelligent optimal design of CMAC neural network for robot manipulators.- Y. Hasegawa, T. Fukuda: Hierarchical behavior controller in robotic applications.- S. Sehad: Neural reinforcement learning for robot navigation.- I. Ahrns, J. Bruske, G. Hailu, G. Sommer: Neural fuzzy techniques in sonar-based collision avoidance.- L. Meeden, D. Kumar: Trends in evolutionary robotics.