Agile autonomous systems are emerging, such as unmanned aerial vehicles (UAVs), that must robustly perform tightly coordinated time-critical missions; for example, military surveillance or search-and-rescue scenarios. In the space domain, execution of temporally flexible plans has provided an enabler for achieving the desired coordination and robustness. We address the challenge of extending plan execution to under-actuated systems that are controlled indirectly through the setting of continuous state variables. Our solution is a novel model-based executive that takes as input a temporally flexible state plan, specifying intended state evolutions, and dynamically generates a near-optimal control sequence. To achieve optimality and safety, the executive plans into the future, framing planning as a disjunctive programming problem. To achieve robustness to disturbances and tractability, planning is folded within a receding horizon, continuous planning framework. Key to performance is a problem reduction method based on constraint pruning. We benchmark performance through a suite of UAV scenarios using a hardware-in-the-loop testbed.
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