An evolutionary algorithm for multi-robot unsupervised learning

Abstract

Based on evolutionary computation principles, an algorithm is presented for learning safe navigation of multiple robot systems. It is a basic step towards automatic generation of sensorimotor control architectures for completing complex cooperative tasks while using simple reactive mobile robots. Each individual estimates its own performance, without requiring any supervision. When two robots meet each other, the proposed crossover mechanism allows them to improve the mean performance index. In order to accelerate the evolution and prevent the population from staying in a local maximum, an adaptive self-mutation is added: the mutation rate is made dependent on the individual performance. Computer simulations and experiments using a team of real mobile robots have demonstrated the rapidity of convergence to the best-expected solution.