@inproceedings{b3e9e89031f8497692cb8c8caed7da48,
title = "A Novel UAV Path Planning Method Based on Layered PER-DDQN",
abstract = "Path planning is a key technology for Unmanned Aerial Vehicles (UAVs) to complete the operational mission in a complex battlefield environment. A step-by-step path planning method based on the Layered Double Deep Q-Network with Prioritized Experience Replay (Layered PER-DDQN) is proposed in this paper. The novel method is constructed by combining the threat avoidance network and collision-free network based on the PER-DDQN framework. By analyzing the current environment of the UAV, the networks output threat avoidance action vector and obstacle avoidance action vector, and the method does a weighted summation of the action vectors according to the weight of the subproblems to obtain the final action. The simulation experiment verifies that the Layered PER-DDQN path planning method has better convergence and practicability than the Deep Q-Network and A* algorithm.",
keywords = "Double deep Q-network, Path planning, Prioritized experience replay, UAV",
author = "Weixiang Wang and An Zhang and Wenhao Bi and Zeming Mao and Minghao Li",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; Asia-Pacific International Symposium on Aerospace Technology, APISAT 2021 ; Conference date: 15-11-2021 Through 17-11-2021",
year = "2023",
doi = "10.1007/978-981-19-2635-8_51",
language = "英语",
isbn = "9789811926341",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "693--702",
editor = "Sangchul Lee and Cheolheui Han and Jeong-Yeol Choi and Seungkeun Kim and Kim, {Jeong Ho}",
booktitle = "The Proceedings of the 2021 Asia-Pacific International Symposium on Aerospace Technology APISAT 2021, Volume 2",
}