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Route Planning for Unmanned Air Vehicles with Multiple Missions Using an Evolutionary Algorithm

In this paper, the route-planning problem for unmanned air vehicles with multiple missions is studied with the proposal of a novel evolutionary route planner. In the new planner, the individual candidates are evaluated with respect to the workspace. Therefore the computation of the configuration space is avoided. With digital terrain elevation data, our approach can find a near-optimal route that can increase the surviving probability efficiently. By using a problem-specific representation of candidate solutions and genetic operators, our algorithm can take into account different kinds of mission constraints and generate the solutions in real-time.