基于遗传算法的无人机三维运动规划

2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ) Pub Date : 2019-11-01 DOI:10.1109/HNICEM48295.2019.9072878

Maverick Rivera, J. R. D. del Rosario, A. Bandala

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引用次数: 1

摘要

无人驾驶飞行器(UAV)的发展是目前研究的一个热门领域。在大多数应用中，为了找到最优路径并避开障碍物，都需要寻路算法。在本文中，为了确定无人机避开沿途障碍物的最优路径，实现了一种遗传算法。所实现的遗传算法采用变长染色体来解决这一问题。仿真结果表明，该系统平均进行了29代迭代，避免了53500次碰撞，从而找到了最佳路径。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Genetic Algorithm Based 3D Motion Planning for Unmanned Aerial Vehicle

Development of Unmanned Aerial Vehicle (UAV) is now a popular field in research. In most of its applications, a pathfinding algorithm is needed in order to find the optimal path and avoid obstacles. In this paper, a genetic algorithm is implemented in order to determine the optimal path for a UAV that will avoid obstacles along the way. The genetic algorithm implemented uses variable-length chromosomes to solve the problem. The results of the simulation of the system yield an average of 29 generations and avoided 53, 500 collisions to find the best path.

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2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM )

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