This book contains an edited collection of eighteen contributions on soft and hard computing techniques and their applications to autonomous robotic systems. Each contribution has been exclusively written for this volume by a leading researcher. The volume demonstrates the various ways that the soft computing and hard computing techniques can be used in different integrated manners to better develop autonomous robotic systems that can perform various tasks of vision, perception, cognition, thinking, pattern recognition, decision-making, and reasoning and control, amongst others. Each chapter of the book is self-contained and points out the future direction of research.
"It is a must reading for students and researchers interested in exploring the potentials of the fascinating field that will form the basis for the design of the intelligent machines of the future"
(Madan M. Gupta)

Basic Principles and Methodologies.- Where is Knowledge in Robotics? Some Methodological Issues on Symbolic and Connectionist Perspectives of AI.- to Fusion Based Systems ¿ Contributions of Soft Computing Techniques and Application to Robotics.- Planning and Navigation.- Applied Soft Computing Strategies for Autonomous Field Robotics.- Integration of Reactive Utilitarian Navigation and Topological Modeling.- Line-Crawling Robot Navigation: A Rough Neurocomputing Approach.- Hierarchical Planning in a Mobile Robot for Map Learning and Navigation.- An Analytical Method for Decomposing the External Environment Representation Task for a Robot with Restricted Sensory Information.- Evolutionary Artificial Potential Field ¿ Applications to Mobile Robot Path Planning.- Learning, Adaptation and Control.- Using Hierarchical Fuzzy Behaviors in the RoboCup Domain.- A Bio-Inspired Robotic Mechanism for Autonomous Locomotion in Unconventional Environments.- Online Learning and Adaptation for Intelligent Embedded Agents Operating in Domestic Environments.- Integration of Soft Computing Towards Autonomous Legged Robots.- Grasp Learning by Active Experimentation Using Continuous B-Spline Model.- Online Adaptive Fuzzy Neural Identification and Control of Nonlinear Dynamic Systems.- Hybrid Fuzzy Proportional-Integral plus Conventional Derivative Control of Robotics Systems.- Vision and Perception.- Robot Vision Using Cellular Neural Networks.- Multiresolution Vision in Autonomous Systems.- A Computer Vision Based Human-Robot Interface.