Mission assignment model and simulation tool for different types of unmanned aerial vehicles
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The use of unmanned aerial vehicles on the battlefield becomes more and more important every day. Parallel to this growing demand, there is a need for robust algorithms to solve the mission assignment problem in an optimum way. There are several tools for solving the assignment problem and testing the results to evaluate the robustness of the proposed algorithm. For most of the models, input factors are limited to the most important ones to make the process simpler. The aim of this thesis is to create an optimal solution for the assignment problem and test its robustness with a stochastic simulation tool. To accomplish the goals more factors, such as ground abort rates of the UAVs and the area weather risk levels are added. These factors, which were typically excluded from previous studies, are incorporated to make the model more realistic. The analysis and the results proved that the assignment algorithm works well and creates plausible results.
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