Sensor-Based Dynamic Assignment in Distributed Motion Planning

Abstract

Distributed motion planning of multiple agents raises fundamental and novel problems in control theory and robotics. Recently, one such great challenge has been the development of motion planning algorithms that dynamically assign targets or destinations to multiple homogeneous agents, not relying on any a priori assignment of agents to destinations. In this paper, we address this challenge using two novel ideas. First, we develop distributed multi-destination potential fields able to drive every agent to any available destination for almost all initial conditions. Second, we propose sensor-based coordination protocols that ensure that distinct agents are assigned to distinct destinations. Integration of the overall system results in a distributed, multi-agent, hybrid system for which we show that the mutual exclusion property of the final assignment is guaranteed for almost all initial conditions. Moreover, we show that our dynamic assignment algorithm converges after exploring at most a polynomial number of assignments, dramatically reducing the combinatorial nature of purely discrete assignment problems. Our scalable approach is illustrated with nontrivial computer simulations.