Adaptive Division-of-Labor Control Algorithm for Multi-Robot Systems

Abstract

An advanced function for multi-robot systems is the division of labor. There are some studies proposing a multi-agent reinforcement learning method for a division of labor. However, it often requires much time to converge. Many studies focusing on division-of-labor control inspired biological phenomenon have been reported. In those methods, whether heterogeneous or homogeneous state is determined by self-organization, however, group performance improvement is not guaranteed because decentralized control is typically complicated. In this study, we propose adaptive division-of-labor control, enabling adaptive selection of homogeneous or heterogeneous group state. We demonstrate the adaptability of proposal method versus working conditions and address the performance improvement by mathematical analysis. To evaluate the effectiveness of the proposed method, we treat foraging by multi-robot systems and confirm that the robot group inevitably organizes the division of labor with group performance improvement in computer simulations.