CONFLICT RESOLUTION STRATEGY BASED ON DEEP REINFORCEMENT LEARNING FOR AIR TRAFFIC MANAGEMENT

IF 0.8 Q3 ENGINEERING, AEROSPACE
Dong Sui, Chenyu Ma, Jintao Dong
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引用次数: 0

Abstract

With the continuous increase in flight flows, the flight conflict risk in the airspace has increased. Aiming at the problem of conflict resolution in actual operation, this paper proposes a tactical conflict resolution strategy based on Deep Reinforcement Learning. The process of the controllers resolving conflicts is modelled as the Markov Decision Process. The Deep Q Network algorithm trains the agent and obtains the resolution strategy. The agent uses the command of altitude adjustment, speed adjustment, or heading adjustment to resolve a conflict, and the design of the reward function fully considers the air traffic control regulations. Finally, simulation experiments were performed to verify the feasibility of the strategy given by the conflict resolution model, and the experimental results were statistically analyzed. The results show that the conflict resolution strategy based on Deep Reinforcement Learning closely reflected actual operations regarding flight safety and conflict resolution rules.
基于深度强化学习的空中交通管理冲突解决策略
随着飞行流量的不断增加,空域内的飞行冲突风险也随之增加。针对实际作战中的冲突解决问题,提出了一种基于深度强化学习的战术冲突解决策略。控制器解决冲突的过程被建模为马尔可夫决策过程。Deep Q Network算法对智能体进行训练,得到解决策略。agent使用调整高度、调整速度或调整航向的命令来解决冲突,奖励功能的设计充分考虑了空中交通管制规则。最后,通过仿真实验验证了冲突解决模型所给出策略的可行性,并对实验结果进行了统计分析。结果表明,基于深度强化学习的冲突解决策略较好地反映了飞行安全和冲突解决规则的实际操作情况。
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来源期刊
Aviation
Aviation ENGINEERING, AEROSPACE-
CiteScore
2.40
自引率
10.00%
发文量
20
审稿时长
15 weeks
期刊介绍: CONCERNING THE FOLLOWING FIELDS OF RESEARCH: ▪ Flight Physics ▪ Air Traffic Management ▪ Aerostructures ▪ Airports ▪ Propulsion ▪ Human Factors ▪ Aircraft Avionics, Systems and Equipment ▪ Air Transport Technologies and Development ▪ Flight Mechanics ▪ History of Aviation ▪ Integrated Design and Validation (method and tools) Besides, it publishes: short reports and notes, reviews, reports about conferences and workshops
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