An Aircraft Collision Avoidance Method Based on Deep Reinforcement Learning

Zuocheng Liu, Evgeny Neretin, Xiaoguang Gao, Kaifang Wan

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

Compared to existing traffic alert and collision avoidance systems (TCAS), the development of the new Airborne Collision Avoidance System X (ACAS X) adopts a model-based optimization approach to enhance airspace safety and operational efficiency. However, limitations such as the generation of massive numerical tables during development and the separation of development and evaluation processes hinder the system's maintenance and further application in avionics systems. Therefore, in this study, we tackle the aircraft collision avoidance problem using deep reinforcement learning methods, which substantially reduce storage requirements and enable self-updating during interaction with the environment, thus streamlining the development process. Our contributions include constructing a simulation environment for aircraft collision avoidance and establishing a reward system. Through three different reinforcement learning methods, we address collision avoidance while considering aircraft scheduling issues. Simulation results demonstrate the effectiveness of reinforcement learning in tackling aircraft collision avoidance and airspace scheduling problems.

源语言英语
主期刊名2024 9th International Conference on Control and Robotics Engineering, ICCRE 2024
出版商Institute of Electrical and Electronics Engineers Inc.
241-246
页数6
ISBN(电子版)9798350372694
DOI
出版状态已出版 - 2024
活动9th International Conference on Control and Robotics Engineering, ICCRE 2024 - Hybrid, Osaka, 日本
期限: 10 5月 202412 5月 2024

出版系列

姓名2024 9th International Conference on Control and Robotics Engineering, ICCRE 2024

会议

会议9th International Conference on Control and Robotics Engineering, ICCRE 2024
国家/地区日本
Hybrid, Osaka
时期10/05/2412/05/24

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