Coordinating the Collective Behavior of Swarm Robotics Systems Based on Incremental Evolution

Abstract

A swarm robotics system is a distributed system consisting of many homogeneous autonomous robots without a global controller. In this paper, we apply a swarm robotics system to the cooperative food-foraging problem, in which autonomous cooperation is necessary to carry out the foraging task. Supposing that each robot in a swarm has an artificial neural network as its behavioral controller, the evolutionary robotics approach is adopted for robot learning. However, we quickly discover that this approach is inadequate because the conventional evolutionary robotics approach does not coordinate a sufficiently adaptive collective behavior within a limited number of evaluations. As a consequence, we instead apply the incremental evolution approach to develop a sophisticated cooperative collective behavior. The results of a series of computer simulations demonstrate that the incremental evolution approach is effective for solving this difficult type of problem.