Genetic Evolution of a Neural Network for the Autonomous Control of a Four-Wheeled Robot

In this paper we exercise the genetic programming of a artificial neural network (ANN) that integrates sensor vision, path planning and steering control of a mobile robot. The training of the ANN is done by a simulation of the robot, its sensors, and environment. The results of each simulation run are then used to denote the ability for the tested network to operate the robot. After less than hundred evaluations we receive an ANN that is able to navigate the robot around obstacles better than a traditional implementation of sensor-based vision and navigation for the same robot.