Real Time Control of a Khepera Robot using Genetic Programming

A computer language is a very general form of representing and specifying an autonomous agent's behavior. The task of planning feasible actions could then simply be reduced to an instance of automatic programming. We have evaluated the use of an evolutionary technique for automatic programming called Genetic Programming (GP) to directly control a miniature robot. To our knowledge, this is the rst attempt to control a real robot with a GP based learning method. Two schemes are presented. The objective of the GP system in our rst approach is to evolve real-time obstacle avoiding behavior. This technique enables real-time learning with a real robot using genetic programming. It has, however, the drawback that the learning time is limited by the response dynamics of the environment. To overcome this problems we have devised a second method, learning from past experiences which are stored in memory. This new system allows a speed-up of the algorithm by a factor of more than 2000. Obstacle avoiding behavior emerges much faster, approximately 40 times as fast, allowing learning of this task in 1.5 minutes. This learning time is several orders of magnitudes faster then comparable experiments with other control architectures. Furthermore, the GP algorithm is very compact and can be ported to the micro-controller of the autonomous mobile miniature robot.