Receding Horizon based Cooperative Vehicle Control with Optimal Task Allocation

The problem of cooperative multi-target interception in an uncertain environment is investigated in this thesis. The targets arrive in the mission space sequentially at a priori unknown time instants and a priori unknown locations, and then move on a priori unknown trajectories. A group of vehicles with known dynamics are employed to visit the targets as quickly and efficiently as possible. To this end, a time-discounting reward is defined for each target which can be collected only if one of the vehicles visits that target. A cooperative receding horizon scheme is designed, which predicts the future positions of the targets and maximizes the estimate of the expected total collectible rewards, accordingly. The problem is initially investigated for the case when there are a finite number of targets arriving in the mission space sequentially. It is shown that the number of targets that are not visited by any vehicle in the mission space will be sufficiently small if the targets arrive sufficiently infrequently. The problem is then generalized to the case of infinite number of targets and a finite-time convergence analysis is also presented. A more practical case where the vehicles have limited sensing and communication ranges is also investigated using a game-theoretic approach. The problem is then solved for the case when a cluster of vehicles is required to visit each target. Simulations confirm the efficacy of the proposed strategies