Behavior-based motion planning for group control

Despite the large body of work in both motion planning and multi-agent simulation, little work has focused on the problem of planning motion for groups of robots using external ¿controller¿ agents. We call this problem the group control problem. This problem is complex because it is highly underactuated, dynamic, and requires multi-agent cooperation. In this paper, we present a variety of new motion planning algorithms based on EST, RRT, and PRM methods for shepherds to guide flocks of robots through obstacle-filled environments. We show using simulation on several environments that under certain circumstances, motion planning can find paths that are too complicated for nai¿ve ¿simulation only¿ approaches. However, inconsistent results indicate that this problem is still in need of additional study.