A Path Planning for One UAV Based on Geometric Algorithm

Haochen Li, Sentang Wu, Pengzhi Xie, Zekui Qin, Baochang Zhang
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引用次数: 1

Abstract

In this paper, a new learning algorithm named geometric learning algorithm is proposed to solve the UAV’s track planning problem. Actually, based on the environment modeling, the optimal path planning problem is to find an optimal route. The Geometric learning algorithm is essentially an reinforcement learning algorithm. It can not only fully use the distance information to calculate the track based on the geometric distance information but can also fuse dangerous information in a complex environment, which solves the problem of track planning from a practical and theoretical point of view. Based on the two-dimensional successful planning of a single drone, the algorithm is extended to the path planning and decision making of single drone three-dimensional planning. And from a practical and theoretical point of view, the path planning problem has been well solved.
基于几何算法的无人机路径规划
针对无人机的航迹规划问题,提出了一种新的学习算法——几何学习算法。实际上,基于环境建模的最优路径规划问题就是寻找最优路径。几何学习算法本质上是一种强化学习算法。它不仅可以充分利用距离信息来计算基于几何距离信息的轨道,而且可以融合复杂环境下的危险信息,从实践和理论的角度解决了轨道规划问题。在单架无人机二维成功规划的基础上,将该算法扩展到单架无人机三维规划的路径规划和决策。从实践和理论的角度来看,很好地解决了路径规划问题。
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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