MONODA: a neural modular architecture for obstacle avoidance without knowledge of the environment

Abstract

A technique is proposed to detect and avoid obstacles for a mobile robot in an unknown environment. The usual problem of having too much sensorial information is dealt with by using several neural networks that cooperate in the guidance of the robot. Several unknown obstacle configurations were presented to the modular networks, proving that the MONODA architecture is very effective for obstacle avoidance when there is neither a priori nor a posteriori maps of the environment.