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We consider a setting where multiple vehicles form a team cooperating to visit multiple target points and collect rewards associated with them. The team objective is to maximize the total reward accumulated over a given time interval. Complicating factors include uncertainties regarding the locations of target points and the effectiveness of collecting rewards, differences among vehicle capabilities, and the fact that rewards are time-varying. We propose a receding horizon (RH) controller suitable for dynamic and uncertain environments, where combinatorially complex assignment algorithms are infeasible. The control scheme dynamically determines vehicle trajectories by solving a sequence of optimization problems over a planning horizon and executing them over a shorter action horizon. This centralized scheme can generate stationary trajectories in the sense that they guide vehicles to target points, even though the controller is not explicitly designed to perform any discrete point assignments. This paper establishes conditions under which this stationarity property holds in settings that are analytically tractable, quantifies the cooperative properties of the controller, and includes a number of illustrative simulation examples.