分布式MAS 在飞行冲突解脱中的应用研究

交通运输系统工程与信息 ›› 2015, Vol. 15 ›› Issue (5): 231-238.

分布式MAS 在飞行冲突解脱中的应用研究

周建^*1，Ahmed RAHMANI²，刘昕¹，王莉莉¹

1. 中国民航大学空中交通管理学院，天津300300; 2. 里尔中央理工学院自动化、信息技术工程和信号实验室，里尔59650，法国

收稿日期:2015-05-19 修回日期:2015-06-26 出版日期:2015-10-25 发布日期:2015-10-28
作者简介:周建（1983-），男，江西宜春人，讲师，硕士.
基金资助:
国家自然科学基金资助(U1333116)；国家空管科研课题(GKG201405002)；中央高校基本科研业务费中国民航大学专项基金资助(ZXH2013D013).

Application of Distributed MAS in Flight Conflict Avoidance

ZHOU Jian¹, RAHMANI Ahmed², LIU Xin¹,WANG Li-li¹

1. School of Air Traffic Management, Civil Aviation University of China, Tianjin 300300, China; 2. Institute of Automatic, Information Engineering and Signal, Ecole Centrale de Lille, Lille 59650, France

Received:2015-05-19 Revised:2015-06-26 Online:2015-10-25 Published:2015-10-28

摘要/Abstract

摘要：

在自由飞行的环境下，为解决飞行冲突探测与解脱（conflict detection and resolution，CDR）问题，提出一种基于高度层、航向和速度调配的综合解脱方法，并将多 agent 系统(multi-agent system, MAS) 的分布式技术与启发式算法相结合，进行问题求解. 首先设计了分布式MAS框架结构，然后建立了飞行冲突探测模型，高度层调配模型及航向、速度调配模型，最后，综合运用了基于合同网协议的分布式算法和自适应遗传算法进行问题求解.仿真实验表明，所设计的MAS框架是可行的，同时分布式算法和自适应遗传算法的综合应用能很快找到基于高度层、航向和速度分配的近似最优解，为CDR问题提供了新的解决思路.

关键词: 航空运输, 冲突解脱, 合同网协议, 多agent系统, 空中交通管理

Abstract:

In order to solve the problem of flight conflict detection and resolution (CDR) in the background of free flight, an integrated CDR method based on assignment of flight level, heading and velocity is proposed, and distributed technology of MAS (multi-agent system) and a heuristic algorithm are combined for the algorithm implementation. Firstly, a framework of distributed MAS is designed. Secondly, a conflict detection model, a flight level allocation model and a heading & velocity assignment model are established. Finally, a distributed algorithm based on contract net protocol and an adaptive genetic algorithm are designed to solve the problem. Simulation results show that the MAS framework is feasible, and the combination of the designed distributed algorithm and adaptive genetic algorithm can search the approximate optimal solution rapidly, based on the allocation of flight level, heading and velocity, which provides a new solution to the CDR problem.

Key words: air transportation, conflict resolution, contract net protocol, multi- agent system, air traffic management

中图分类号:

周建，Ahmed RAHMANI，刘昕，王莉莉. 分布式MAS 在飞行冲突解脱中的应用研究[J]. 交通运输系统工程与信息, 2015, 15(5): 231-238.

ZHOU Jian, RAHMANI Ahmed, LIU Xin,WANG Li-li. Application of Distributed MAS in Flight Conflict Avoidance[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(5): 231-238.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.tseit.org.cn/CN/abstract/abstract19087.shtml

http://www.tseit.org.cn/CN/Y2015/V15/I5/231

参考文献

[1] Kuchar J K，Yang L C. A review of conflict detection and resolution modeling methods[J]. IEEE Transactions on Intelligent Transportation Systems，2000，1(4)：179- 189.

[2] Hojjat E，Kamyar N. A comparison framework for conflict detection and resolution multi agent modeling methods in air traffic management[J]. International Journal of Information Technology， Control and Automation(IJITCA)，2012，2(4)：51-64.

[3] David S，Premysl V，Michal pechoucek. Agentbased cooperative decentralized airplane-collision avoidance[J]. IEEE Transactions on Intelligent Transportations Systems，2011，12(1)：36-46.

[4] Miguel A V，Colin G. Application of distributed artificial intelligence in autonomous aircraft operations[C]. 20th Conference on Digital Avionics Systems. Daytona Beach，FL：IEEE，2001：7B3/1-7B3/14.

[5] Michael H，George M，Stefan R. An agent based framework for control of merging air-traffic[C]. In Proceedings 16th IFAC Symposium on Automatic Control in Aerospace. St. Petersburg，Russia, 2004.

[6] Magnus L，Andrew L. The OASIS air traffic management system[C]. Proceedings of the Second Pacific Rim International Conference on Artificial Intelligence， PRICAI. Seoul，Korea, 1992.

[7] 刘红红，杨兆升. 基于神经网络的实时交通信号控制与仿真研究[J]. 交通运输系统工程与信息，2008，8 (2)：43- 47. [LIU H H, YANG Z S. Real-time traffic signal control and simulation based on neural networks[J]. Journal of Transportation Systems Engineering and Information Technology, 2008, 8 (2): 43-47.]

[8] 石文先. 基于MAS 协商机制的冲突解脱研究[D]. 南京：南京航空航天大学，2008. [SHI W X. A cooperative MAS approach to conflict resolution[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2008.]

[9] 冯兴杰，赵睿. 多目标遗传算法在飞行冲突解脱中的应用[J]. 计算机工程与设计，2014，35(7)：2577-2581.[FENG X J, ZHAO R. Conflict resolution of airplanes based on multi-objective genetic algorithm[J]. Computer Engineering and Design, 2014, 35(7): 2577-2581.]

[10] 王渊，孙秀霞，刘树光，等. 基于改进人工蜂群算法的多机飞行冲突解脱策略[J]. 空军工程大学学报（自然科学版），2014，15(3)：10-14. [WANG Y, SUN X X, LIU S G, et al. Research on multi-aircraft confliction resolution based on a modified artificial bee colony algorithm[J]. Journal of Air Force Engineering University (Natural Science Edition), 2014, 15(3): 10- 14.]

[11] 裴志刚，李华星，王庆胜. 模拟退火遗传算法在飞行冲突解脱中的应用[J]. 交通与计算机，2005，1(23)： 115-117. [PEI Z G, LI H X, WANG Q S. Application of simulated annealing/genetic algorithms in flight conflict resolution[J]. Traffic and Computer, 2005, 1(23): 115- 117.]

[12] 郭茜，聂润兔. 改进人工势场法在解决飞行冲突问题中的应用[J]. 交通与计算机，2008，5(26)：103-106. [GUO Q, NIE R T. Application of improved artificial field method in aircraft conflict resolution[J]. Traffic and Computer, 2008, 5(26): 103-106.]

[13] 崔莉薇，石为人，刘祥明, 等. 基于遗传粒子群算法的飞行冲突解脱[J]. 计算机工程与应用，2013，49(7)： 263- 266. [CUI L W, SHI W R, LIU X M, et al. Air conflict resolution based on genetic algorithm and particle swarm optimization[J]. Computer Engineering and Applications, 2013, 49(7): 263-266.]

[14] 魏志强. 基于最小成本的高度能力计算方法[J]. 交通运输工程学报，2005，5(2)：77-79. [WEI Z Q. Altitude capability calculation methods based on minimum flight cost[J]. Journal of Traffic and Transportation Engineering, 2005, 5(2):77-79.]

[15] Smith R G，Davis R. The contract net protocol：Highlevel communication and control in a distributed problem solver[J]. IEEE Tran Computers，1980，C- 29 (12)：1104-1113.

[16] Srinivas M， Patnaik L M. Adaptive probabilities of crossover and mutation in genetic algorithms[J]. IEEE Trans on Systems，Man and Cybernetics，1994，24(4)： 656-667.