A Study on Path Planning Algorithms of UAV Collision Avoidance

被引：0

作者：

Xu Z. ^{[1
]}

Hu J. ^{[2
]}

Ma Y. ^{[1
]}

Wang M. ^{[2
]}

Zhao C. ^{[2
]}

机构：

[1] School of Electronics and Information, Northwestern Polytechnical University, Xi'an

[2] School of Automation, Northwestern Polytechnical University, Xi'an

来源：

Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University | 2019年 / 37卷 / 01期

关键词：

Ant colony algorithm; Artificial potential field; Collision avoidance; Fuzzy logic algorithm; Path planning; Unmanned aerial vehicles;

D O I：

10.1051/jnwpu/20193710100

中图分类号：

学科分类号：

摘要：

The unmanned aerial vehicle (UAV) has been a research hotspot worldwide. The UAV system is developing to be more and more intelligent and autonomous. UAV path planning is an important part of UAV autonomous control and the important guarantee of UAV's safety. For the purpose of improving the collision avoidance and path planning algorithms, the artificial potential field, fuzzy logic algorithm and ant colony algorithm are simulated respectively in the static obstacle and dynamic obstacle environments, and compared based on the minimum avoidance distance and range ratio. Meanwhile, an improved algorithm of artificial potential field is proposed, and the improvement helps the UAV escape the local minimum by introducing the vertical guidance repulsion. The simulation results are rigorous and reliable, which lay a foundation for the further fusion of multiple algorithms and improving the path planning algorithms. © 2019 Journal of Northwestern Polytechnical University.

引用

页码：100 / 106

页数：6

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