China Safety Science Journal ›› 2026, Vol. 36 ›› Issue (4): 142-151.doi: 10.16265/j.cnki.issn1003-3033.2026.04.0144

• Safety Technology and Engineering • Previous Articles     Next Articles

Aircraft swarm improved velocity obstacle methods considering low carbon and deviation

Zhong Qingwei1,2(), Yu Yingxue1,3, Liu Su1,**(), Wang Rui1, Guo Jingwei4, Pan Weijun2   

  1. 1 School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan Sichuan 618307, China
    2 Key Laboratory of Flight Techniques and Flight Safety, Civil Aviation Flight University of China, Guanghan Sichuan, 618307, China
    3 School of Electromechanical Engineering, Guangzhou City Construction College, Guangzhou Guangdong 510925, China
    4 Faculty of Business, City University of Macau, Macau 999078, China
  • Received:2025-11-02 Revised:2026-01-10 Online:2026-04-28 Published:2026-10-28
  • Contact: Liu Su

Abstract:

In order to address the requirements of low-carbon operation and safety in aircraft conflict resolution, an IVO method based on adaptive elliptical protected zones was proposed, taking into account aircraft carbon emissions and speed adjustment deviations. First, adaptive elliptical protected zones were constructed according to the velocity differences among aircraft. Second, aircraft flying on the same route with similar velocity vectors and close spatial proximity were grouped into clusters, and key characteristics such as cluster centroids, velocity vectors, and safety regions were defined. Then, a cluster control algorithm was applied to reasonably adjust the velocity vectors of aircraft within each cluster, ensuring the maintenance of safe separation during conflict resolution. Subsequently, velocity obstacle cones were established between clusters. By incorporating constraints on the maximum allowable adjustment per unit time and combining planar geometric analysis with a mathematical optimization model, conflict-free velocities and headings with minimal deviation were determined under low-carbon objectives. Finally, dynamic behaviour analysis was conducted through numerical simulations implemented in Python. The results show that, compared with the traditional velocity obstacle method, the proposed approach improves conflict resolution efficiency by 87.5%, reduces the average adjustment magnitude by 86.67%, and achieves fuel savings of up to 37.36%, demonstrating its effectiveness for aircraft conflict resolution and operational optimization in complex air traffic environments.

Key words: low carbon, velocity adjustment deviation, aircraft swarm, improved velocity obstacle(IVO) method, aircraft conflict resolution

CLC Number: