Apply incremental evolution with CMA-NeuroES controller for a robust swarm robotics system

A distributed autonomous robotic system consisted of many homogeneous autonomous robots without a global controller is often called a swarm robotics system. It is expected that a swarm of robots achieves much more complex tasks than the ones that a single robot can do. However, it is also well known that designing a robot controller for this system is generally quite difficult because the system level behavior is emerged from a result of many dynamical interactions within the system itself or between the system and its environment. In this paper, a robot controller is represented by a recurrent artificial neural network designed by environmental complexification with covariance matrix adaptation evolution strategy, i.e., CMA-NeuroES, adopted for the incremental artificial evolution method. Computer simulations are conducted to prove that the incremental artificial evolution with CMA-NeuroES generates the most robust robot controller among the ones tested in the simulated experiments.