Self-organized synchronization 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 behavior. Owing to these properties, synchronization appears particularly interesting for swarm robotics systems, as it allows robust temporal coordination of the group while minimizing the complexity of the individual controllers. The goal of the experiments presented in this paper is the study of self-organizing synchronization for robots that present an individual periodic behavior. In order to design the robot controllers, we make use of artificial evolution, which proves to be capable of synthesizing minimal synchronization strategies based on the dynamical coupling between robots and environment. The obtained results are analyzed under a dynamical system perspective, which allows us to uncover the evolved mechanisms and to predict the scalability properties of the self-organizing synchronization with respect to varying group size.
['Vito Trianni', 'Stefano Nolfi']
Self-Organizing Sync in a Robotic Swarm: A Dynamical System View