Adaptive action selection without explicit communication for multi-robot box-pushing

This paper describes a novel action selection method for multiple mobile robots box-pushing in a dynamic environment. The robots are designed to need no explicit communication, and be adaptive to a dynamic environments by changing modules of behaviors. Though it is a significant problem to deal with adaptive action selection for multiple mobile-robots in a dynamic environment, few studies have been done. Decentralized control of robots without explicit communication is also practical and important for robustness. Thus we propose adaptive action selection without explicit communication for multi-robot boxpushing, which changes an available behavior set depending on a situation. First four situations are defined with two parameters: existence of other robots and task difficulty. Next we design a set of behaviors for each situations, and mobile robots are programmed to act with behavior-based approach. We fully implement our method on four real mobile robots, and make experiments in dynamic environments.