A receding horizon approach to generating dynamically feasible plans for vehicles that operate over large areas

We present an approach to planning dynamically feasible vehicle trajectories for applications where the vehicle operates in very large environments and for which a kinematic model is poor approximation of the vehicle's dynamics. Using a standard receding horizon control framework, we compute a sequence of short-horizon optimal trajectories that can be computed in real-time. We also compute a global path that is updated only occasionally. Because the environment is very large, we presume that the global path cannot be updated in real-time. By selecting a terminal state cost in the finite-time optimal control problem that approximates the cost-to-go, we are able to propose a formal condition under which the sequence of dynamically feasible trajectories is guaranteed to converge to a desired goal location. Moreover, we show that the global path need be updated only when the formal condition is not satisfied, which allows us to delay update of the global path.