Learning of mobile robots using perception-based genetic

This paper deals with adaptation, evolution, and learning for a mobile robo t based on fuzzy controllers. If its facing environment is stable, the beha vior of a mobile robot can be optimized by conventional genetic algorithms (GAs). Otherwise, the behavior should be tuned by learning according to the change of its environment. However, it is difficult for a mobile robot to maintain various behaviors suitable to changing environmental states. There fore, this paper proposes a GA based on the perceiving information about th e dynamic environment, which is called a Perception-Based GA (PerGA). We ap ply the proposed method for acquiring collision avoidance behaviors of a mo bile robot in a dynamic environment. Furthermore, we conduct several comput er simulations and simple experiments of a mobile robot. Simulation results show that the PerGA can maintain various behaviors according to environmen tal changes. (C) 2001 Elsevier Science Ltd. All rights reserved.