Social Distancing in Robot Swarms: Modulating Exploitation and Exploration Without Signal Exchange

Abstract

The field of swarm robotics draws most of its inspiration from (eu)social animals, which leads to the creation of bio-inspired algorithms. In this study, we show that counterintuitively - seemingly asocial behavior can also lead to successful problem solving performed by a swarm. We decided to test our new algorithm at first with real robots as a proof of concept, because this approach is of high conceptual novelty. We show that our social distancing algorithm (SocDist) performs similarly effective as a simple version of the well established bio-inspired social algorithm BEECLUST in a laboratory experiment, while avoiding some of its drawbacks. In additional agent-based computer simulation experiments we show that such an `asocial' component within a swarm robotic algorithm can lead to a significant performance increase. Beside its effectiveness, the SocDist approach also leads to specific spatial distributions of the swarm robots, which may be useful for practical applications.