End-to-End Learning-Based Obstacle Avoidance for Fixed-Wing UAVs

Teng Wang, Zhao Xu, Jinwen Hu, Haozhe Zhang, Zhiwei Chen

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

In this paper, a deep reinforcement learning training framework based on attitude control is proposed for the obstacle avoidance problem of fixed-wing UAVs in 3D realistic scenes. The training framework includes an environment information feature extraction network and an obstacle avoidance strategy training network. In the training network module, attitude stabilization and altitude maintenance reward and punishment functions are designed for fixed-wing UAV attitude control. The learning of obstacle avoidance strategies is performed while the fixed-wing UAV maintains attitude stability and altitude stability. Finally, the method is validated in the joint simulation environment we built. The training method introduced in this paper and the built training framework provide simulation data and results for the fixed-wing UAV-aware avoidance strategy in a realistic environment, which provides a feasible solution for the deployment of the obstacle avoidance strategy network to real aircraft.

源语言英语
主期刊名Proceedings of 2022 International Conference on Autonomous Unmanned Systems, ICAUS 2022
编辑Wenxing Fu, Mancang Gu, Yifeng Niu
出版商Springer Science and Business Media Deutschland GmbH
3342-3351
页数10
ISBN(印刷版)9789819904785
DOI
出版状态已出版 - 2023
活动International Conference on Autonomous Unmanned Systems, ICAUS 2022 - Xi'an, 中国
期限: 23 9月 202225 9月 2022

出版系列

姓名Lecture Notes in Electrical Engineering
1010 LNEE
ISSN(印刷版)1876-1100
ISSN(电子版)1876-1119

会议

会议International Conference on Autonomous Unmanned Systems, ICAUS 2022
国家/地区中国
Xi'an
时期23/09/2225/09/22

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