Multiple-Aircraft-Conflict Resolution Under Uncertainties

被引:12
作者
Zhao, Peng [1 ]
Erzberger, Heinz [2 ]
Liu, Yongming [1 ]
机构
[1] Arizona State Univ, Sch Engn Matter Transport & Energy, 501 E Tyler Mall, Tempe, AZ 85287 USA
[2] NASA Ames Res Ctr, Mailstop 210-9, Moffett Field, CA 94035 USA
关键词
AIR-TRAFFIC MANAGEMENT; AVOIDANCE; DECONFLICTION; OPTIMIZATION;
D O I
10.2514/1.G005825
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
A new method for efficient trajectory planning to resolve potential conflicts among multiple aircraft is proposed. A brief review of aircraft trajectory planning and conflict resolution methods is given first. Next, a new method is proposed that is based on a probabilistic conflict risk map using the predicted probability of conflict with the intention information of the intruders. The risk-map-based method allows the path planning algorithm to simultaneously account for many uncertainties affecting safety, such as positioning error, wind variability, and human errors. Following this, A* algorithm is used to find the cost-minimized trajectory for a single aircraft by considering all other aircraft as intruders. Search heuristic method is implemented to iterate the A* algorithm for all aircraft to optimize the trajectory planning. Convergence and computation efficiency of the proposed method are investigated in detail. Numerical examples are used to illustrate the effectiveness of the proposed method under several important scenarios for air traffic control, such as wind effects, non-cooperative aircraft, minimum disturbance of pilots, and deconflict with flight intent information. Several conclusions are drawn based on the proposed method.
引用
收藏
页码:2031 / 2049
页数:19
相关论文
共 49 条
  • [41] Schouwenaars T., 2004, P AIAA GUID NAV CONT, P5141
  • [42] Conflict resolution for air traffic management: A study in multiagent hybrid systems
    Tomlin, C
    Pappas, GJ
    Sastry, S
    [J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1998, 43 (04) : 509 - 521
  • [43] Improving Air Traffic Management with a Learning Multiagent System
    Tumer, Kagan
    Agogino, Adrian
    [J]. IEEE INTELLIGENT SYSTEMS, 2009, 24 (01) : 18 - 21
  • [44] van den Berg J, 2011, SPRINGER TRAC ADV RO, V70, P3
  • [45] Toward individual-sensitive automation for air traffic control using convolutional neural networks
    van Rooijen S.J.
    Ellerbroek J.
    Borst C.
    van Kampen E.
    [J]. Journal of Air Transportation, 2020, 28 (03): : 105 - 113
  • [46] Cooperation of combinatorial solvers for en-route conflict resolution
    Wang, Ruixin
    Alligier, Richard
    Allignol, Cyril
    Barnier, Nicolas
    Durand, Nicolas
    Gondran, Alexandre
    [J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2020, 114 : 36 - 58
  • [47] Yang X., 2018, INT C RES AIR TRANSP, P8
  • [48] Zeghal Karim., 1998, Guidance, Navigation, and Control Conference and Exhibit, page, P4240
  • [49] Online Multiple-Aircraft Collision Avoidance Method
    Zhao, Peng
    Wang, Weichang
    Ying, Lei
    Sridhar, Banavar
    Liu, Yongming
    [J]. JOURNAL OF GUIDANCE CONTROL AND DYNAMICS, 2020, 43 (08) : 1456 - 1472