Evolution-algorithm-based unmanned aerial vehicles path planning in complex environment

被引：23

作者：

Liu, Xiaolei ^{[1
]}

Du, Xiaojiang ^{[2
]}

Zhang, Xiaosong ^{[3
]}

Zhu, Qingxin ^{[1
]}

Guizani, Mohsen ^{[4
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu, Sichuan, Peoples R China

[2] Temple Univ, Dept Comp & Informat Sci, Philadelphia, PA 19122 USA

[3] Univ Elect Sci & Technol China, Ctr Cyber Secur, Chengdu, Sichuan, Peoples R China

[4] Qatar Univ, Dept Comp Sci & Engn, Doha, Qatar

来源：

COMPUTERS & ELECTRICAL ENGINEERING | 2019年 / 80卷

基金：

中国国家自然科学基金;

关键词：

UAV; Dynamic planning; Path planning; Evolution algorithm; UAV; PROTOCOL;

D O I：

10.1016/j.compeleceng.2019.106493

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

With the wide application of Unmanned Aerial Vehicles (UAVs) in production and life, more and more attention has been paid to the autonomous track planning of UAVs. When UAV path planning algorithm is dealing with flying in an unknown complex environment, there are some problems, such as inability to dynamically plan the track and slow speed to calculate the path. This paper proposes a dynamic path planning based on an improved evolutionary optimization algorithm. The experimental results show that the evolutionary optimization algorithm based on improved t-distribution can effectively deal with the problems of high computational complexity and low search efficiency encountered in UAV dynamic track planning. It has strong robustness and can dynamically plan the appropriate track. (C) 2019 Elsevier Ltd. All rights reserved.

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