Deep Reinforcement Learning Based Trajectory Planning for Multi-UAV Cooperative Data Collection

Yuqi Miao, Lei Lei, Lijuan Zhang*

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

In the context of UAV trajectory planning for data collection, challenges such as the uncertainty of a large-scale dynamic unknown environment and the need for multi-UAV coordination are prevalent. To address these challenges, this paper proposes a UAV data collection trajectory planning algorithm based on the D3QN (Double Dueling Deep Q-Network) algorithm. The proposed algorithm enables multiple UAVs to dynamically plan their flight paths for data collection in unknown environments through centralized training and distributed application. The algorithm’s performance is improved by incorporating competition mechanisms, candidate node queues, and reward function reshaping techniques. Based on the simulation results, the proposed algorithm outperforms similar algorithms in terms of success rates and task durations.

源语言英语
主期刊名Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology
编辑Jian Dong, Long Zhang, Deqiang Cheng
出版商Springer Science and Business Media Deutschland GmbH
101-108
页数8
ISBN(印刷版)9789819727568
DOI
出版状态已出版 - 2024
已对外发布
活动2nd International Conference on Internet of Things, Communication and Intelligent Technology, IoTCIT 2023 - Xuzhou, 中国
期限: 22 9月 202324 9月 2023

出版系列

姓名Lecture Notes in Electrical Engineering
1197
ISSN(印刷版)1876-1100
ISSN(电子版)1876-1119

会议

会议2nd International Conference on Internet of Things, Communication and Intelligent Technology, IoTCIT 2023
国家/地区中国
Xuzhou
时期22/09/2324/09/23

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引用此

Miao, Y., Lei, L., & Zhang, L. (2024). Deep Reinforcement Learning Based Trajectory Planning for Multi-UAV Cooperative Data Collection. 在 J. Dong, L. Zhang, & D. Cheng (编辑), Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology (页码 101-108). (Lecture Notes in Electrical Engineering; 卷 1197). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-97-2757-5_11