Chaotic mimic robots.

In this paper, chaos is applied to the control of moving robots in order to generate random-like trajectories needed in tasks such as exploration, scanning natural terrains or mapping of unknown environments. Synchronization between the robots of a team is achieved by exploiting the paradigm of mirror neurons, i.e. a neural structure playing a key role in the process of imitation and behaviour understanding. The experimental results discussed in the paper demonstrate that the introduced approach can be successfully applied to implement an efficient learning system for mobile robots.