Self-organised synchronisation is a common phenomenon observed in many natural and artificial systems: simple coupling rules at the level of the individual components of the system result in an overall coherent behaviour. Owing to these properties, synchronisation appears particularly interesting for swarm robotics systems, as it allows for robust temporal coordination of the group while minimising the complexity of the individual controllers. The goal of the experiments presented in this paper is the study of self-organising synchronisation for robots that present an individual periodic behaviour. In order to design the robot controllers, we make use of artificial evolution, which proves to be capable of synthesising minimal synchronisation strategies based on the dynamical coupling between robots and environment. The obtained results are analysed under a dynamical system perspective, which allows us to uncover the evolved mechanisms and to predict the scalability properties of the self-organising synchronisation with respect to varying group size.

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Swarm engineering is the development of a swarm of cooperating agents commensurate to some criterion designed to produce a global outcome. Once the swarm has satisfied this criterion, it will accomplish the desired task. We present a theoretical formalism for understanding how swarm-based TSP optimization algorithms work. We generate a swarm criterion and show that two of the algorithms of Marco Dorigo satisfy these conditions, but to differing degrees. Finally, we present a new method of tackling the problem based on the tunneling of agents through the grid which satises the swarm criterion. Results on three standard TSP problems found in the TSPLIB standard library are presented.