航空学报 > 2009, Vol. 30 Issue (12): 2342-2347

散点状分布危险天气区域下的航班改航路径规划

李雄1, 徐肖豪2, 赵嶷飞2, 卫东选1   

  1. 1.南京航空航天大学 民航学院 2.中国民航大学 空管学院
  • 收稿日期:2008-10-21 修回日期:2009-02-16 出版日期:2009-12-25 发布日期:2009-12-25
  • 通讯作者: 李雄

Flight Rerouting Path Planning in Dispersedly Distributed Severe Weather Areas

Li Xiong1, Xu Xiaohao2, Zhao Yifei2, Wei Dongxuan1   

  1. 1.College of Civil Aviation, Nanjing University of Aeronautics and Astronautics 2.College of Air Traffic Management, Civil Aviation University of China
  • Received:2008-10-21 Revised:2009-02-16 Online:2009-12-25 Published:2009-12-25
  • Contact: Li Xiong

摘要: 针对沿航线散点状分布的危险天气区域影响下的航班改航问题,提出了基于多目标遗传算法(MOGA)的航班改航路径规划方法。首先建立了基于网格的改航环境模型,并给出散点状分布危险天气区域的描述方法。然后以改航航段的航段距离、平均偏离距离和转弯点个数为目标,应用带精英保留策略的非支配排序遗传算法(NSGA-II)对改航路径规划进行研究,提出了适用于改航路径规划的编码方法,同时引入了删除算子。最后,以昆明—广州航线为例,研究了散点状分布危险天气区域下的改航路径规划,并与基于多边形的改航路径规划算法作了比较。仿真结果表明:采用本文方法运行一次即可得到多条安全、可行的改航路径,且无需先验知识,为决策者选择改航路径提供了充足的依据。

关键词: 空中交通管制, 遗传算法, NSGA-II, 改航, 路径规划

Abstract: In order to cope with the flight rerouting problem caused by dispersedly distributed severe weather areas along the flight path, a new rerouting path planning method based on the multi-objective genetic algorithm (MOGA) is proposed. First, a grid-based environment model of the air traffic rerouting problem is constructed, and a method to describe dispersedly distributed severe weather areas is given. Then non-dominated sorting genetic algorithm II (NSGA-II) is applied to the flight rerouting problem, which takes into consideration the distance, number of turns and deflection of the flight rerouting path. Furthermore, a new coding method and the deletion operator are applied. Finally, the flight rerouting paths of Kunming-Guangzhou with disper-sedly distributed severe weather areas are studied, and compared with the rerouting method based on the polygon algorithm. Simulation results show that each time the proposed method can find a set of safe and feasible flight rerouting paths without prior information, from which decision-makers can select the most appropriate one.

Key words: air traffic control, genetic algorithms, non-dominated sorting genetic algorithm II, rerouting, path planning

中图分类号: