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.

This course consists of lectures, exercises involving multiple-choice tests (MyCourse Quizzes), and a student project which will be peer-graded. You can freely collect points via the various course activities (quizzes and/or projects). In particular, you can reach the top grade solely by achieving full points in the multiple-choice tests or via the student project. There is no minimum requirement for any of the course activities. Thus you can freely decide where (quizzes and/or student project) to collect points.