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

机译:受限环境下基于遗传算法的无人机二维路径规划

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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:可以通过找到优化问题的解决方案来完成无人飞行器(UAV)避开障碍物的路径规划。这是一个很难解决的问题,尤其是在控制点数量很多时。进化算法已经成为这类NP-Hard问题的一种选择。遗传算法可以很好地解决无人机路径规划的优化问题,并且可以在可接受的时间内获得可接受的解决方案。本文试图给出一个答案,即如何通过避开诸如NOTAM区域,雷达站点,建筑物等的禁区,在二维环境中为无人机完成适当的路径规划。遗传算法的使用表示为TSP问题域。针对这一目标,本文还提出了有关无人机路径规划的理论结构。结果表明,所提出的思想可以为自主的单无人机提供安全的路径。

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