Abstract:
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.
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