Event-Driven Cooperative Receding Horizon Control for Multi-Agent Systems in Uncertain Environments

We propose an event-driven Cooperative Receding Horizon (CRH) controller to solve maximum reward collection problems (MRCP) where multiple agents cooperate to maximize the total reward collected from a set of targets in a given mission space. In previous work, a CRH controller was developed and in this paper, we overcome several limitations of this controller, including potential instabilities in the agent trajectories and poor performance due to inaccurate estimation of a reward-to-go function. Rewards are non-increasing functions of time and the environment is uncertain with new targets detected by agents at random time instants. The controller sequentially solves optimization problems over a planning horizon and executes the control for a shorter action horizon, where both are defined by certain events associated with new information becoming available. In contrast to the earlier CRH controller, we reduce the originally infinite-dimensional feasible control set to a finite set at each control update event. We prove some properties of this new controller and include simulation results showing its improved performance.