UAV Trajectory Planning Based on Deep Residual Learning Network Optimization

被引：0

作者：

Liu, Yinghuang ^{[1
]}

Lu, Zhi ^{[1
]}

Tian, Xizhe ^{[1
]}

Hou, Rui ^{[1
]}

机构：

[1] Nanchang Hangkong Univ, Nanchang, Jiangxi, Peoples R China

来源：

TRENDS IN ADVANCED UNMANNED AERIAL SYSTEMS, ICAUAS 2024 | 2025年

基金：

中国国家自然科学基金;

关键词：

UAV; Trajectory Planning; Deep Residual Learning; Optimization;

D O I：

10.1007/978-981-96-3240-4_6

中图分类号：

V [航空、航天];

学科分类号：

08 ; 0825 ;

摘要：

Trajectory planning plays a crucial role in UAV flight to improve its efficiency and safety. Particle swarm optimization algorithm (PSO) is a popular trajectory planning method, but it can be plagued by local minima and computational complexity. In this paper, we address these issues by introducing a deep residual learning method to optimize the parameters of PSO to improve trajectory planning for UAVs. The PSO algorithm before and after the improvement is compared using a benchmark test function, which reveals the search advantages of the improved PSO algorithm. Simulation experiments are also conducted in 3D maps for comparison, proving the effectiveness of the improved PSO algorithm trajectory planning.

引用

页码：52 / 58

页数：7

共 15 条

[11] Aggregated Residual Transformations for Deep Neural Networks [J].

Xie, Saining ;

Girshick, Ross ;

Dollar, Piotr ;

Tu, Zhuowen ;

He, Kaiming .

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017), 2017, :5987-5995

[12]

Yang DC, 2021, CHINA COMMUN, V18, P136, DOI 10.23919/JCC.2021.02.009

[13]

Zagoruyko S, 2017, Arxiv, DOI arXiv:1605.07146

[14]

Zhang X., 2018, IEEE Access, V6, P14409

[15] Multi-UAV Trajectory Planning for Energy-Efficient Content Coverage: A Decentralized Learning-Based Approach [J].

Zhao, Chenxi ;

Liu, Junyu ;

Sheng, Min ;

Teng, Wei ;

Zheng, Yang ;

Li, Jiandong .

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2021, 39 (10) :3193-3207

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