GPU implementation of food-foraging problem for evolutionary swarm robotics systems

Evolutionary swarm robotics (ESR) is an artificial evolution approach to generating meaningful swarm behavior in multi-robot systems which typically consist of many homogenous autonomous robots in which the same robot controllers designed with evolving artificial neural networks are employed. Historically speaking, this approach has been thought to be a promising approach for swarm robotics systems (SRS), because the swarm behavior is an emergent phenomenon caused by many local interactions among autonomous robots and it is very hard to give a program to each robot for appropriate swarm behavior in advance. However, its realization is considered to be impractical even for a simulated SRS because the artificial evolution requires a very large computational cost. In this paper, in order to overcome this computational cost problem, a novel implementation method, i.e., the parallel problem solving using graphics processing units (GPUs) and OpenMP on a multi-core CPU, is introduced. The efficiency of the proposed method is demonstrated with the food-foraging problem with an evolving SRS.