Systems Engineering and Electronics ›› 2023, Vol. 45 ›› Issue (5): 1429-1440.doi: 10.12305/j.issn.1001-506X.2023.05.19

• Systems Engineering • Previous Articles    

Conflict resolution strategy based on flight conflict network and genetic algorithm

Kexin BI1,2, Minggong WU1,2, Xiangxi WEN1,2,*, Wenbin ZHANG3, Wenda YANG1,2   

  1. 1. Air Traffic Control and Ground Controlled Interception College, Air Force Engineering University, Xi'an 710051, China
    2. National Key Laboratory of Air Traffic Collision Prevention, Xi'an 710051, China
    3. Unit 31435 of the PLA, Shenyang 110015, China
  • Received:2021-07-20 Online:2023-04-21 Published:2023-04-28
  • Contact: Xiangxi WEN

Abstract:

Aiming at the problem of multi-aircraft flight conflict in airspace, a flight conflict resolution method based on flight conflict network and genetic algorithm is proposed. Firstly, the conflict between aircraft nodes is judged according to the aircraft position, heading and speed state. And a flight conflict network model is built. The speed obstacle method is used to to determine the edges and weights in the network. Secondly, according to different scenarios, the flight conflict resolution methods are divided into speed resolution, heading resolution and compound resolution, and the resolution ranges are deduced respectively. On this basis, a fitness function is constructed according to the complexity of the flight conflict network, and the genetic algorithm is used to encode and solve the flight conflict problem. Simulation experiments show that the proposed method can provide a reasonable and feasible solution for the flight conflict resolution under different control situations according to the control needs and the controller's ability. It can complete the adjustment of 8 aircraft within 18 s and eliminate more than 12 pairs. Compared with other multi-aircraft conflict resolution methods, the proposed method has high timeliness and stronger conflict resolution ability.

Key words: flight conflict network, flight conflict resolution, velocity obstacle method, genetic algorithm, air traffic management

CLC Number: 

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