摘要
随着航空运输的不断发展,空中交通系统面临着越来越严重的航线拥挤,飞行冲突日益频繁,给现有的空中交通管理系统带来前所未有的压力。自由飞行是解决该问题的有效途径,即允许飞行员选择最适合自己的飞行速度和飞行路线。飞行冲突的探测和解决是实现自由飞行的关键问题,需要找到合理可靠的空中交通飞行冲突解决方法,为自由飞行提供理论基础。
本文主要研究人工势场法和蚁群算法在空中交通飞行冲突解决中的应用。首先对空中交通冲突解决问题的研究背景以及研究现状进行系统的概述,随后引入人工势场法和蚁群算法,并基于这两种算法,建立了各自相应的数学模型。在应用人工势场法解决飞行冲突时,对传统的势场法进行了改进,把飞行冲突的解决从二维空间扩展到三维空间。在应用蚁群算法解决飞行冲突时,首先应用了基本蚁群算法对问题进行求解;然后将人工势场法的规划结果作为蚁群算法的先验知识,对蚁群算法进行初始化,提出了人工势场法和蚁群算法相结合的方法。与基本蚁群算法相比,这种相结合的方法使得运算效率和结果准确性得到了明显提高,这也正是本文的创新之处。最后,通过对多架飞机汇聚飞行冲突问题的仿真模拟计算,表明本文所采用的算法能够通过航向的改变较好地解决空中交通飞行冲突问题,为自由飞行和空管自动化系统的研究提供理论基础。
With the development of air transportation, the air traffic system is facing more and more congestions. Flight conflicts have appeared frequently, putting unprecedented pressure on the current air traffic control system. Part of the anticipated solution is free flight, which allows the pilots to choose the airway and flight speeds suitable for them. Aircraft conflict detection and resolution are key problems for free flight. It is required to find out an automated and feasible conflict resolution method, which provides theoretical basis for free flight.
This paper mainly focuses on the application of Artificial Field method and Ant Colony Algorithm(ACA for short) in air traffic conflicts resolution problem. Firstly, the background of the conflict detection and resolution research being undertaken worldwidely is systematically introduced. Then Artificial Field method and ACA are introduced and based on each method, two corresponding mathematic models are established. As to the application of Artificial Field method, traditional Artificial Field method is improved to resolve air traffic conflicts in 3-D space instead of 2-D space; as to the application of ACA, after using Basic Ant Colony Algorithm(BACA for short) to resolve aircraft conflict , a improved ACA is proposed by incorporating Artificial Field method into BACA. The Artificial Potential Field is introduced as the priori knowledge to initialize the solution of BACA. Compared with BACA, the proposed method incorporating these two methods notably improves the planning quality and efficiency, which is the innovative point in this paper. Finally, by the simulation of multi-aircraft centering conflicts, the algorithms are proved to be competent in resolving various air traffic conflicts by heading change,which provide theoretical basis for further air traffic automation research.
引文
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