Improving Multi-Robot Behavior Using Learning-Based Receding Horizon Task Allocation

Planning efficient and coordinated policies for a team of robots is a computationally demanding problem, especially when the system faces uncertainty in the outcome or duration of actions. In practice, approximation methods are usually employed to plan reasonable team policies in an acceptable time. At the same time, many typical robotic tasks include a repetitive pattern. On the one hand, this multiplies the increased cost of inefficient solutions. But on the other hand, it also provides the potential for improving an initial, inefficient solution over time. In this paper, we consider the case that a single mission specification is given to a multi-robot system, describing repetitive tasks which allow the robots to parallelize work. We propose here a decentralized coordination scheme which enables the robots to decompose the full specification, execute distributed tasks, and improve their strategy over time.