Robotic Control Using Hierarchical Genetic Programming

Marcin L. Pilat¹ and Franz Oppacher²

¹Department of Computer Science, University of Calgary, 2500 University Drive N.W., Calgary, AB, T2N 1N4, Canada
pilat@cpsc.ucalgary.ca

²School of Computer Science, Carleton University, 1125 Colonel By Drive, Ottawa, ON, K1S 5B6, Canada
oppacher@scs.carleton.ca

Abstract. In this paper, we compare the performance of hierarchical GP methods (Automatically Defined Functions, Module Acquisition, Adaptive Representation through Learning) with the canonical GP Implementation and with a linear genome GP system in the domain of evolving robotic controllers for a simulated Khepera miniature robot. We successfully evolve robotic controllers to accomplish obstacle avoidance, wall following, and light avoidance tasks.

LNCS 3103, p. 642 ff.

Full article in PDF

lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2004