流量拥堵空域内一种基于Q-Learning算法的改航路径规划
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U8

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中国民用航空飞行学院科研面上项目(J2021-082)


A Rerouting Path Planning Based on Q-Learning Algorithm in Traffic Congestion Airspace
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General Program of Civil Aviation Flight University of China(J2021-082)

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    摘要:

    目前,空中流量激增导致空域资源紧张的问题越发凸显,为了缓解这一现状,将基于流量管理层面对航空器进行改航路径的研究。首先采用栅格化的方式对空域环境进行离散化处理,根据航路点流量的拥挤程度把空域划分为三种不同类型的栅格区域。其次通过改进强化学习中马尔科夫决策过程的奖励函数对其进行建模,并基于 策略运用Q-Learning算法对该模型进行迭代求解,对相应的参数取值进行探究比较以提高结果的可适用性。最后经过仿真运行,计算出不同参数赋值下的最优路径及相应的性能指标。研究结果表明:应用该模型和算法可以针对某一时段内的流量拥堵空域搜索出合适的改航路径,使飞机避开流量拥挤的航路点,缩短空中延误时间,有效改善空域拥堵的现况。

    Abstract:

    At present, the problem of airspace resource shortage caused by the surge of air traffic is becoming more and more prominent. In order to alleviate this situation, the study of aircraft rerouting based on the flow management would be carried out. Firstly, the airspace environment was discretized by rasterization, and the airspace was divided into different types of raster regions according to the congestion degree of waypoint flow. Secondly, it was used to model by improving the reward function of Markov decision process in reinforcement learning. Based on the strategy, Q-Learning algorithm was used for iterative solution, and the corresponding parameter value was explored and compared to improve the applicability of the results. Finally, the optimal path and the corresponding performance indexes under different parameter assignments were calculated through simulation. The results show that the model and algorithm can be used to search for an appropriate rerouting path for the congested airspace in a certain period of time, so that the aircraft can avoid the congested waypoints, shorten the delay time in the air, effectively improve the status of airspace congestion.

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向征,何雨阳,全志伟. 流量拥堵空域内一种基于Q-Learning算法的改航路径规划[J]. 科学技术与工程, 2022, 22(32): 14494-14501.
XIANG Zheng, HE Yuyang, QUAN Zhiwei. A Rerouting Path Planning Based on Q-Learning Algorithm in Traffic Congestion Airspace[J]. Science Technology and Engineering,2022,22(32):14494-14501.

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  • 收稿日期:2022-01-21
  • 最后修改日期:2022-08-14
  • 录用日期:2022-06-03
  • 在线发布日期: 2022-12-05
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