Date

2018

Author

Karabay, Nail

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In this thesis, we study real-time routing of an unmanned air vehicle (UAV) in a twodimensional dynamic environment. The UAV starts from a base point, visits all targets and returns to the base point, while all targets change their locations during the mission period. We find the best route for the route planner (RP) considering two objectives; minimization of distance and minimization of radar detection threat. We develop a real-time algorithm to find the UAV’s most preferred route for a RP who has an underlying linear or quadratic preference function. In this algorithm, we structure the nondominated frontiers of the trajectories between each target pair and find a route using these trajectories. The algorithm updates the route of the UAV each time the UAV arrives at a target. As the UAV must return to the base target at the end of its journey, we solve a multi-objective shortest Hamiltonian path problem to find a route rather than a multi-objective traveling salesperson problem each time the UAV visits a target. To reduce the computational burden, we develop k-closest heuristic. In this heuristic, instead of structuring the nondominated frontiers between all target pairs, for each target, we select k closest targets and structure only the nondominated vi frontiers of these k targets. In addition, we develop an adaptive algorithm to determine the value of k. For the RP who has a quadratic preference function, we choose among {u1D45B} nondominated trajectories for each target pair to find a route. We consider the cases n = 1 and n > 1, seperately. We demonstrate all algorithms on different examples

Subject Keywords

Vehicles, Remotely piloted, Drone aircraft.

URI

http://etd.lib.metu.edu.tr/upload/12621821/index.pdf
https://hdl.handle.net/11511/27150

Collections

Graduate School of Natural and Applied Sciences, Thesis