Three-dimensional path planning for UAV based on improved quantum-behaved brain storming optimization algorithm

被引：0
|
作者：
Sun X. [1 ]
Ding Z. [1 ]
Cai C. [2 ]
Pan S. [1 ]
机构：
[1] School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing
[2] School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan
来源：
Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2024年 / 52卷 / 01期
关键词：
convergence precision; evolution mechanism; path planning; quantum-behaved brain storm optimization(QBSO); unmanned aerial vehicle;
D O I：
10.13245/j.hust.240416
中图分类号：
学科分类号：
摘要：
Considering the problem of path planning for unmanned aerial vehicle(UAV) in complex environment,a three-dimensional path planning method for UAV based on improved quantum-behaved brain storm optimization(QBSO) algorithm was proposed.In the early stage of the evolution process,two populations evolved independently,thereby improving the global search ability of the algorithm. In the late stage of evolution process,individuals in each population were ranked and those individuals ranked in the top half in each population formed a new population.Then,the new population continued to evolve according to the evolution mechanism of QBSO,which accelerated the convergence speed of the algorithm. In addition,to further improve the global search ability of the algorithm,an improved generation method for individuals to be mutated was proposed.Experimental results show that the path planner based on the improved QBSO algorithm outperforms the BSO,quantum-behaved BSO,improved BSO and global-best BSO algorithms based path planners in terms of explorability,convergence precision and stability. © 2024 Huazhong University of Science and Technology. All rights reserved.
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收藏
页码：112 / 117
页数：5
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