Vehicle Study with Neural Networks

Abstract

The biology is characteristic of biologic phototaxis and negative phototaxis. Can a machine be endowed with such a characteristic? This is the question we study in this paper, so a method of realizing vehicle's phototaxis and negative phototaxis through a neural network is presented. A randomly generated network is used as the main computational unit. Only the weights of the output units of this network are changed during training. It will be shown that this simple type of a biological realistic neural network is able to simulate robot controllers like that incorporated in Braitenberg vehicles. Two experiments are presented illustrating the stage-like study emerging with this neural network.