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
  • [31] Solving the Air Conflict Resolution Problem Under Uncertainty Using an Iterative Biobjective Mixed Integer Programming Approach
    Lehouillier, Thibault
    Nasri, Moncef Ilies
    Soumis, Francois
    Desaulniers, Guy
    Omer, Jeremy
    [J]. TRANSPORTATION SCIENCE, 2017, 51 (04) : 1242 - 1258
  • [32] Markov Decision Process-Based Distributed Conflict Resolution for Drone Air Traffic Management
    Ong, Hao Yi
    Kochenderfer, Mykel J.
    [J]. JOURNAL OF GUIDANCE CONTROL AND DYNAMICS, 2017, 40 (01) : 69 - 80
  • [33] Conflict probability estimation for free flight
    Paielli, RA
    Erzberger, H
    [J]. JOURNAL OF GUIDANCE CONTROL AND DYNAMICS, 1997, 20 (03) : 588 - 596
  • [34] Tactical Conflict Alerting Aid for Air Traffic Controllers
    Paielli, Russell A.
    Erberger, Heinz
    Chiu, Danny
    Heere, Karen R.
    [J]. JOURNAL OF GUIDANCE CONTROL AND DYNAMICS, 2009, 32 (01) : 184 - 193
  • [35] Tactical Conflict Resolution Using Vertical Maneuvers in En Route Airspace
    Paielli, Russell A.
    [J]. JOURNAL OF AIRCRAFT, 2008, 45 (06): : 2111 - 2119
  • [36] Conflict resolution problems for air traffic management systems solved with mixed integer programming
    Pallottino, L
    Feron, EM
    Bicchi, A
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2002, 3 (01) : 3 - 11
  • [37] Negotiated Decentralized Aircraft Conflict Resolution
    Pritchett, Amy R.
    Genton, Antoine
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2018, 19 (01) : 81 - 91
  • [38] Refai M., 2019, P AIAA SCITECH FOR J, P1476, DOI DOI 10.2514/6.2019-1476
  • [39] Subliminal Speed Control in Air Traffic Management: Optimization and Simulation
    Rey, David
    Rapine, Christophe
    Fondacci, Remy
    El Faouzi, Nour-Eddin
    [J]. TRANSPORTATION SCIENCE, 2016, 50 (01) : 240 - 262
  • [40] Sathyan A., 2017, 20171751 AIAA, DOI [10.2514/6.2017-1751, DOI 10.2514/6.2017-1751]