The invention discloses a real-time three-dimensional multi-machine conflict resolution method based on graph reinforcement learning, and the method comprises the steps: constructing a three-dimensional air traffic interaction scene, carrying out the modeling of the scene into a graph, and obtaining the convolution features of the graph as model input; and establishing a conflict buffer model, and designing a reward function to guide the aircraft to minimize an additional flight distance while ensuring a safety interval. A D3QN reinforcement learning algorithm is adopted for training, the optimal avoidance action of the aircraft is obtained, and real-time multi-aircraft conflict resolution in the high-density airspace is achieved.
本发明公开了一种基于图强化学习的实时三维多机冲突解脱方法,本发明构建了三维空中交通交互场景并将其建模为图,获得图卷积特征作为模型输入。建立冲突缓冲模型,设计奖励函数引导航空器在保证安全间隔的同时最小化额外飞行距离。采用D3QN强化学习算法训练,获得航空器最佳避让动作,实现了高密度空域实时多机冲突解脱。
Multi-machine real-time three-dimensional conflict resolution method based on graph reinforcement learning
一种基于图强化学习的多机实时三维冲突解脱方法
2025-02-18
Patent
Electronic Resource
Chinese
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen |
Multi-UAV Conflict Resolution with Graph Convolutional Reinforcement Learning
ArXiv | 2021
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