Evolving collision avoidance on autonomous robots

Abstract

Utilizing the collective behavior of a population of interacting individuals, based on rather simple local algorithms, is a promising approach for achieving complex goals. We use an onboard online evolutionary model, based on finite Moore automata, to develop collective behavior in an artificial swarm of micro-robots. Experiments have been made in simulation to achieve Collision Avoidance. The model is shown to be capable to generate the desired behavior and we present experiments for adjusting the parameters of the evolutionary optimization. © 2008 International Federation for Information Processing.