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2D path planning of UAVs with genetic algorithm in a constrained environment

机译:约束环境中具有遗传算法的无人机的2D路径规划

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Path planning of an Unmanned Aerial Vehicle (UAV) for avoiding obstacles can be accomplished by finding a solution to an optimization problem. It is a hard problem to solve, especially when the number of control points is high. Evolutionary algorithms have emerged as a choice for this type of NP-Hard problems. The Genetic Algorithm may be good for solving the optimization problems for path planning of UAV, and this algorithm may achieve an acceptable solution in an acceptable time. In this paper, it is tried to give an answer about that how an appropriate path planning for a UAV can be done in the 2-dimensional environment by avoiding Forbidden Zones such as NOTAM areas, radar sites, buildings, etc. Usage of genetic algorithm is presented as TSP problem domain. For this target, the theoretical structure about the UAV path planning is also presented in the paper. The results showed that, the proposed idea can supply safe paths for autonomous single UAVs.
机译:用于避免障碍物的无人空中车辆(UAV)的路径规划可以通过找到优化问题的解决方案来实现。解决是一个难以解决的问题,特别是当控制点的数量高时。进化算法已成为这种NP难题的选择。遗传算法可能良好地解决UAV路径规划的优化问题,并且该算法可以在可接受的时间内实现可接受的解决方案。在本文中,试图通过避免禁止区域,雷达站点,建筑物等,通过避免遗传算法等禁区可以在二维环境中完成适当的路径规划的答案。被呈现为TSP问题域。对于该目标,纸张还介绍了关于UAV路径规划的理论结构。结果表明,建议的想法可以为自主单独的无人机提供安全的路径。

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