A Potential Game with a Dynamic Penalization Map for Multi-Robot Cooperative Search Missions

This paper addresses the problem of multi-robot coordinated navigation in target localization missions. We employ a potential game with an added penalization term that is dynamically updated to improve the performance of the multi-robot system by decreasing the number of movements needed to localize the targets and therefore, save time and energy. Then, we give conditions on this penalization map to guarantee low probabilities of revisiting explored zones on consecutive turns. By employing binary log-linear learning (BLLL) we solve the game for different simulated scenarios and compare them to recently developed strategies. Afterwards, we implement a decentralized controller on a robot simulator and illustrate the penalization map on a physical robot in a simple scenario.