Abstract
This paper deals with adaptation, evolution, and learning in collision avoidance problems in a fuzzy-based intelligent robotic system. But the intelligence of a robot depends on the structure of hardware and software for processing information. So we have proposed a robotic system with structured intelligence. We focus on a mobile robotic system with fuzzy controller, and have proposed a sensory network as perception ability for the mobile robot. Therefore if environment is state, it is possible to evolve a robot system. However, if environment changed, it is hard to evolve a robot system promptly. So as maintain of variety, we propose Perception-GA to select crossover's partners according the environment information without increasing fuzzy rule, and as evaluations over internal evaluations, which law changing parameter of function of evaluations and learning rate. We discuss the effectiveness of the proposed method through computer simulation results of Perception-Based Robot.
