Automated Sequencing of Swarm Behaviors for Supervisory Control of Robotic Swarms

Abstract

Robotic swarms are distributed systems that exhibit global behaviors arising from local interactions between individual robots. Each robot can be programmed with several local control laws that can be activated depending on an operator's choice of global swarm behavior. While some simple behaviors (e.g. rendezvous) with guaranteed performance on known objectives under strict assumptions have been studied in the literature, real missions occur in uncontrolled environments with dynamically arising objectives and require combinations of behaviors. Given a library of swarm behaviors, a supervisory operator commanding the swarm must choose a sequence of behaviors to execute in order to accomplish a particular task during a mission composed of many dynamically arising tasks. In this paper, we formalize the problem of finding an optimal behavior sequence to maximize swarm performance on a complex task. Given the swarm behavior library, a set of decision time points and a performance criterion, we present an informed search algorithm that computes the maximum performance behavior sequence. The algorithm is proven to be optimal and complete. A relevant modification is presented that generates bounded suboptimal solutions more quickly. We apply the algorithm to a swarm navigation application and a dynamic area coverage application, demonstrating the utility of our algorithm even in situations where the behaviors in the library have not been designed for the task at hand.