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<p>As unmanned aerial vehicle (UAV) technology and  availability improves, it becomes increasingly more  important to operate UAVs efficiently. Utilizing one UAV at  a time is a relatively simple task, but when multiple UAVs  need to be coordinated, optimal search plans can be  difficult to create in a timely manner. In this thesis, we  create a decision aid that generates efficient routes for  multiple UAVs using dynamic programming and a limited-lookahead  heuristic. The goal is to give the user the best  knowledge of the locations of an arbitrary number of targets  operating on a specified graph of nodes and arcs. The  decision aid incorporates information about detections and  nondetections and determines the probabilities of target  locations using Bayesian updating. Target movement is  modeled by a Markov process. The decision aid has been  tested in two multi-hour field experiments involving actual  UAVs and moving targets on the ground.
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<br>Subjects: Drone aircraft; United States; Search theory; Dynamic programming</div>
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<dl><dd>Nigus, Christopher A.</dd>
<dd>McCadden, Kevin K.</dd></dl></div>
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