Neural Controller for a Mobile Robot in a Nonstationary Environment

Recently it has been introduced a neural controller for a mobile robot that learns both forward and inverse odometry of a diierential-drive robot through an unsupervised learning-by-doing cycle. This article introduces an obstacle avoidance module that is integrated into the neural controller. This module makes use of sensory information to determine at each instant a desired angle and distance that causes the robot to navigate around obstacles on the way to a nal target. Obstacle avoidance is performed in a reactive manner by representing the objects and target in the robot's environment as Gaussian functions. However, the innuence of the Gaussians is modulated dynamically on the basis of the robot's behavior in a way that avoids problems with local minima. The proposed module enables the robot to operate successfully with diierent obstacle conngurations, such as corridors, mazes, doors and even concave obstacles.