Co-evolving controller and sensing abilities in a simulated Mars Rover explorer

The paper presents an evolutionary robotics model of the Rover Mars robot. This work has the objective to investigate the possibility of using an alternative sensor system, based on infrared sensors, for future rovers capable of performing autonomous tasks in challenging planetary terrain environments. The simulation model of the robot and of Mars terrain is based on a physics engine. The robot control system consists of an artificial neural network trained using evolutionary computation techniques. An adaptive threshold on the infrared sensors has been evolved together with the neural control system to allow the robot to adapt itself to many different environmental conditions. The properties of the behavior obtained after the evolutionary process has been tested by measuring the generalization performance of the rover under various terrain conditions and especially under rough terrain conditions. In addition, the dynamics of the co-evolution between the controller and the threshold has been analyzed. Those analyses show that different pathways have been explored by the evolutionary process in order to adapt the sensing abilities and the control system.