In this paper, action of wheeled type robot is determined from the obstacle configuration and the robot's self-state using a neural network (NN), and multiple moving obstacles avoided. In order to avoid obstacles with the minimum movement time, the design parameters of the NN are optimized by genetic algorithm (GA), using the data collected from several environments, in which each environment has a different obstacle configuration.
