@inproceedings{567d351d103f4371b05591544a335441,
title = "Deep Reinforcement Learning Based Trajectory Planning for Multi-UAV Cooperative Data Collection",
abstract = "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{\textquoteright}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.",
keywords = "D3QN, data collection, deep reinforcement learning, trajectory planning, UAV",
author = "Yuqi Miao and Lei Lei and Lijuan Zhang",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; 2nd International Conference on Internet of Things, Communication and Intelligent Technology, IoTCIT 2023 ; Conference date: 22-09-2023 Through 24-09-2023",
year = "2024",
doi = "10.1007/978-981-97-2757-5_11",
language = "English",
isbn = "9789819727568",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "101--108",
editor = "Jian Dong and Long Zhang and Deqiang Cheng",
booktitle = "Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology",
address = "Germany",
}