A new event-driven Cooperative Receding Horizon controller for multi-agent systems in uncertain environments

In previous work, a Cooperative Receding Horizon (CRH) controller was developed for solving cooperative multi-agent problems in uncertain environments. In this paper, we overcome several limitations of this controller. We propose an event-driven CRH controller to solve the maximum reward collection problem (MRCP) where multiple agents cooperate to maximize the total reward collected from a set of stationary targets in a given mission space. 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 time step. We prove some properties of this new controller and include simulation results showing its improved performance.