Evolution, Self-organization and Swarm Robotics

Summary

The activities of social insects are often based on a self-organising process, that is, “a process in which pattern at the global level of a system emerges solely from numerous interactions among the lower-level components of the system”(see Camazine-EtAl:01, p. 8). In a self-organising system such as an ant colony, there is neither a leader that drives the activities of the group, nor are the individual ants informed about a global recipe or blueprint to be executed. On the contrary, each single ant acts autonomously following simple rules and locally interacting with the other ants. As a consequence of the numerous interactions among individuals, a coherent behaviour can be observed at the colony level.

A similar organisational structure is definitely beneficial for a swarm of autonomous robots. In fact, a coherent group behaviour can be obtained providing each robot with simple individual rules. Moreover, the features that characterise a self-organising system—such as decentralisation, flexibility and robustness—are highly desirable also for a swarm of autonomous robots. The main problem that has to be faced in the design of a self-organising robotic system is the definition of the individual rules that lead to the desired collective behaviour. The solution we propose to this design problem relies on artificial evolution as the main tool for the synthesis of self-organising behaviours. In this chapter, we provide an overview of successful applications of evolutionary techniques to the evolution of self-organising behaviours for a group of simulated autonomous robots. The obtained results show that the methodology is viable, and that it produces behaviours that are efficient, scalable and robust enough to be tested in reality on a physical robotic platform.