Autonomous Robotic Systems: Soft Computing and Hard Computing Methodologies and Applications

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.