Task-oriented multi-robot learning in behavior-based systems

A large application domain for multi-robot teams involves task-oriented missions, in which potentially heterogeneous robots must solve several distinct tasks. Previous research addressing this problem in multi-robot systems has largely focused on issues of efficiency, while ignoring the real-world situated robot needs of fault tolerance and adaptivity. This paper addresses this problem by developing an architecture called L-ALLIANCE that incorporates task-oriented action selection mechanisms into a behavior-based system, thus increasing the efficiency of robot team performance while maintaining the desirable characteristics of fault tolerance and adaptivity. We present our investigations of several competing control strategies and derive an approach that works well in a wide variety of multi-robot task-oriented mission scenarios. We provide a formal model of this technique to illustrate how it can be incorporated into any behavior-based system