Surveillance Mission Scheduling with Unmanned Aerial Vehicles in Dynamic Heterogeneous Environments

Abstract:
In this study, we design, evaluate, and compare multiple heuristic techniques for mission scheduling of unmanned aerial vehicles (UAVs) in energy-constrained dynamic environments. These techniques are capable of finding effective mission schedules in real-time, where the schedules determine which UAVs and sensors are used to surveil which targets at any given time. We develop and utilize a surveillance value metric to quantify the effectiveness of mission schedules, incorporating the amount and usefulness of information obtained from surveilling targets. We use the surveillance value metric in simulation studies to evaluate the heuristic techniques in a variety of scenarios generated by a reality-based randomized model, where the scenarios are capable of changing dynamically. We consider two comparison heuristics, three value-based heuristics, and a metaheuristic that intelligently switches between the best value-based heuristics. Additionally, we modify the metaheuristic with preemption and filtering techniques to further improve the schedules it finds. We show that, for all scenarios that we consider, the novel modified metaheuristics find solutions that are the best on average compared to all other techniques that we evaluate.

Full access to this whitepaper requires completing and submitting the following form: