Dynamic Heuristics for Surveillance Mission Scheduling with Unmanned Aerial Vehicles in Heterogeneous Environments

In this study, our focus is on the design of mission scheduling techniques capable of working in dynamic environments with unmanned aerial vehicles, to determine effective mission schedules in real time. The effectiveness of mission schedules for unmanned aerial vehicles is measured using a surveillance value metric, which incorporates information about the amount and usefulness of information obtained from surveilling targets. We design a set of dynamic heuristic techniques, which are compared and evaluated based on their ability to maximize surveillance value in a wide range of scenarios generated by a randomized model. We consider two comparison heuristics, three value-based heuristics, and a metaheuristic that intelligently switches between the best value-based heuristics. The novel metaheuristic is shown to find effective solutions that are the best on average as all other techniques that we evaluate in all scenarios that we consider.