Adaptive multi-robot coordination: A game-theoretic perspective

Multi-robot systems researchers have been investigating adaptive coordination methods for improving spatial coordination in teams. Such methods adapt the coordination method to the dynamic changes in density of the robots. Unfortunately, while their empirical success is evident, none of these methods has been understood in the context of existing formal work on multi-robot learning. This paper presents a reinforcement-learning approach to coordination algorithm selection, which is not only shown to work well in experiments, but is also analytically grounded. We present a reward function (Effectiveness Index, EI), that reduces time and resources spent coordinating, and maximizes the time between conflicts that require coordination. It does this by measuring the resource-spending velocity. We empirically show its success in several domains, including robots in virtual worlds, simulated robots, and physical AIBO robots executing foraging. In addition, we analytically explore the reasons...

• Type: Journal
• Year: 2010
• Where: ICRA
• Authors: Gal A. Kaminka, Dan Erusalimchik, Sarit Kraus
• Keywords: Coordination Method, ICRA 2010, Robotics, Robots, Spatial Coordination