On the evolution of self-organising behaviours in a swarm of autonomous robots

The problem of designing self-organising behaviours for a swarm of robots is tackled resorting to artificial evolution, which proceeds in a bottom-up direction by first defining the controllers at the individual level and then testing their effect at the collective level. In this way, it is possible to bypass the difficulties encountered in the decomposition of the global behaviour into individual ones, and the further encoding of the individual behaviours into the controllers' rules. In the experiments presented in this thesis, we show that this approach is viable, as it produces efficient individual controllers and robust self-organising behaviours. To the best of our knowledge, our experiments are the only example of evolved self-organising behaviours that are successfully tested on a physical robotic platform.

Besides the engineering value, the evolution of self-organising behaviours for a swarm of robots also provides a mean for the understanding of those biological processes that were a fundamental source of inspiration in the first place. In this perspective, the experiments presented in this thesis can be considered an interesting instance of a synthetic approach to the study of collective intelligence and, more in general, of Cognitive Science.