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基于可见光通信的多无人机协同数据采集及路径规划

林天天 何志凯 唐小伟 石运梅 黄逸 马骁

无线电工程2026,Vol.56Issue(3):379-389,11.
无线电工程2026,Vol.56Issue(3):379-389,11.DOI:10.3969/j.issn.1003-3106.2026.03.001

基于可见光通信的多无人机协同数据采集及路径规划

Multi-UAV Cooperative Data Acquisition and Path Planning Based on Visible Light Communication

林天天 1何志凯 2唐小伟 3石运梅 3黄逸 3马骁4

作者信息

  • 1. 同济大学 建筑与城市规划学院,上海 200092||同济大学 电子与信息工程学院,上海 201804
  • 2. 中国航空综合技术研究所,北京 100028
  • 3. 同济大学 建筑与城市规划学院,上海 200092||同济大学 电子与信息工程学院,上海 201804||同济大学 上海智能科学与技术研究院,上海 201210
  • 4. 北京航天控制仪器研究所,北京 100039
  • 折叠

摘要

Abstract

With the advancement of communication technologies,traditional Radio Frequency(RF)communication confronts numerous challenges in electromagnetically sensitive or heavily interfered environments.Visible Light Communication(VLC)possesses advantages such as anti-interference capability,abundant spectrum resources,and high transmission rate,enabling it to ensure the reliability of data transmission in electromagnetically sensitive environments.Equipping Unmanned Aerial Vehicle(UAV)with VLC base stations allows full utilization of the high mobility of UAVs,effectively overcoming the coverage limitations of fixed VLC base stations.Consequently,UAV-aided VLC systems exhibit great potential in data acquisition tasks under complex environments,and are particularly suitable for the efficient collection and transmission of massive node information in the Internet of Things(IoT).VLC communication system with multi-UAV collaboration is studied,and by comprehensively considering the impact of flight jitter on communication channels,a task allocation method based on an improved K-means clustering algorithm is designed.Furthermore,a 3D trajectory planning method for UAVs based on the Twin Delayed Deep Deterministic Policy Gradient(TD3)algorithm is explored.The performance of the proposed methods is verified through simulation experiments,aiming to provide theoretical support and technical references for intelligent communication systems.Simulation results indicate that when the clustering factor w is 0.3,the proposed improved K-means clustering+TD3 path planning algorithm achieves superior system performance compared to baseline algorithms.Specifically,in comparison with baseline algorithms like SCAN,the proposed algorithm can effectively reduce the total flight distance of all UAVs by approximately 56%.

关键词

无人机/可见光通信/数据采集/深度强化学习/路径规划

Key words

UAV/VLC/data acquisition/reinforcement learning/path planning

分类

信息技术与安全科学

引用本文复制引用

林天天,何志凯,唐小伟,石运梅,黄逸,马骁..基于可见光通信的多无人机协同数据采集及路径规划[J].无线电工程,2026,56(3):379-389,11.

基金项目

国家自然科学基金(62388101,62501423,62201391) (62388101,62501423,62201391)

上海市浦江人才计划(22PJD073) (22PJD073)

高速磁浮技术装备路行业工程研究中心开放基金(ERCM-SFCF-2025-003) National Natural Science Foundation of China(62388101,62501423,62201391) (ERCM-SFCF-2025-003)

Shanghai Pujiang Program(22PJD073) (22PJD073)

Engineering Research Center of Railway Industry of High-Speed Maglev Transportation Technology Equipment(ERCM-SFCF-2025-003) (ERCM-SFCF-2025-003)

无线电工程

1003-3106

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