Coordinating agile systems through the model-based execution of temporal plans

TLDR

Agile autonomous systems such as UAVs must robustly perform tightly coordinated, time‑critical missions, and temporally flexible plans have enabled such coordination and robustness in the space domain. The study extends plan execution to underactuated systems that are controlled indirectly through continuous state variables. A novel model‑based executive takes a temporally flexible state plan and dynamically generates a near‑optimal control sequence by solving a disjunctive programming problem within a receding‑horizon continuous planning framework, using constraint pruning to achieve optimality, safety, robustness, and tractability. Performance was benchmarked on UAV scenarios using a hardware‑in‑the‑loop testbed.

Abstract

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 underactuated 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.