Surveillance mission scheduling with unmanned aerial vehicles in dynamic heterogeneous environments

In this study, we design, evaluate, and compare multiple heuristic techniques for mission scheduling of distributed systems comprising unmanned aerial vehicles (UAVs) in energy-constrained dynamic environments. These techniques find effective mission schedules in real-time to determine which UAVs and sensors are used to surveil which targets. We develop 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 with a reality-based randomized model. We consider two comparison heuristics, three value-based heuristics, and a metaheuristic that intelligently switches between the best value-based heuristics. Additionally, preemption and filtering techniques are applied to further improve the metaheuristic. 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.