An artificial neural network structure able to obstacle avoidance behavior used in mobile robots

Abstract

This article presents an artificial neural network (ANN) structure applied to control a mobile robot movement in dynamically changing environments (environments with mobile obstacles). The proposed structure is a backward neural one. So, it is based on past and future positions, and on a optimal pre-established path. The past positions provide the ANN with memory of the mobile robot previous positions. On the other hand, the future positions provide the ANN with a goal, i.e., where the robot should go. Based on this information, the robot do not lose its goal, even if it has to avoid an obstacle. The results show the efficiency of the ANN in a form of simulations.