RSPECT: Robust and Scalable Planner for Energy-Aware Coordination of UAV-UGV Teams in Aerial Monitoring

TL;DR

RSPECT is a scalable heuristic algorithm designed for energy-aware UAV-UGV coordination in aerial monitoring, providing robust plans that adapt to uncertainties and optimize mission completion time.

Contribution

The paper introduces RSPECT, a novel heuristic for robust, energy-aware planning of UAV-UGV teams, addressing NP-hardness with theoretical guarantees and practical efficiency.

Findings

RSPECT produces plans that are robust to uncertainties.

The method significantly reduces planning time compared to exact solutions.

Simulations and experiments validate the effectiveness of RSPECT.

Abstract

We consider the robust planning of energy-constrained unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), which act as mobile charging stations, to perform long-horizon aerial monitoring missions. More specifically, given a set of points to be visited by the UAVs and desired final positions of the UAV-UGV teams, the objective is to find a robust plan (the vehicle trajectories) that can be realized without a major revision in the face of uncertainty (e.g., unknown obstacles/terrain, wind) to complete this mission in minimum time. We provide a formal description of this problem as a mixed-integer program (MIP), which is NP-hard. Since exact solution methods are computationally intractable for such problems, we propose RSPECT, a scalable and efficient heuristic. We provide theoretical results on the complexity of our algorithm and the feasibility and robustness of resulting plans. We also demonstrate the performance of our method via simulations and experiments.