With the increasing density level of airspace, the flawed logic of resolution in air conflict has become a potential hazard to keep flight safety for civil aviation. A powerful decision-support system is needed to identify and resolve potential conflicts on planned trajectory in advance. Existing studies on this subject mainly focus on the centralized means, but seldom consider the decentralized approaches. In this paper, a decentralized method is proposed so that each aircraft can generate the collision-free Reference Business Trajectory (RBT) autonomously, and resolve potential conflicts while conforming to the unified rules. Firstly, a Synchronous Discrete-Time-Discrete-Space trajectory modeling is developed to divide the continuous planned trajectory into multiple trajectory segments according to motion state. Thus, the collision can be accurately located at one certain risky segment, and the corresponding collision time can be acquired precisely. Through a weight analysis of collision time, the critical trajectory segment is determined to implement the task of conflict resolution. Then, the Optimal Reciprocal Collision Avoidance (ORCA) algorithm is adopted and extended to determine the collision-free maneuver with the consideration of direction selectivity. At last, the Trajectory Change Points (TCPs) are achieved by the quadratic program for each aircraft. The proposed method can help aircraft generate collision-free RBT in decentralized way successfully. Several simulations are conducted to confirm the validity and efficiency of the proposed approach.
LinCEHungT-WChenH-Y.TCAS algorithm for general aviation based on ADS-B. Proc IMechE, Part G: J Aerospace Engineering2016; 230: 1569–1591.
2.
DarrSRicksWLemosKA.Safer systems: a NextGen aviation safety strategic goal. IEEE Aerosp Electron Syst Mag2010; 25: 9–14.
3.
Brooker P. SESAR and NextGen: Investing in new paradigms. Journal of Navigation 2008; 61: 195--208.
4.
DurandNBarnierN.Dose ATM need centralized coordination? Autonomous conflict resolution analysis in a constrained speed environment. Air Traffic Control Q2015; 23: 325–346.
5.
PrandiniMHuJLygerosJ, et al. A probabilistic approach to aircraft conflict detection. IEEE Trans Intell Transport Syst2000; 1: 199–200.
6.
OmerJ.A space-discretized mixed-integer linear model for air-conflict resolution with speed and heading maneuvers. Comput Oper Res2015; 58: 75–86.
7.
PaielliRAErzbergerH.Trajectory specification for terminal air traffic control: conflict detection and resolution. J Air Transp (Rest)2019; 27: 51–60.
8.
GuanXZhangXLvR, et al. A large-scale multi-objective flights conflict avoidance approach supporting 4D trajectory operation. Sci China Inf Sci2017; 60: 198–210.
9.
QianXMaoJChenC-H, et al. Coordinated multi-aircraft 4D trajectories planning considering buffer safety distance and fuel consumption optimization via pure-strategy game. Transport Res Part C Emerg Technol2017; 81: 18–35.
10.
BrookerP.The Überlingen accident: macro-level safety lessons. Saf Sci2008; 46: 1483–1508.
11.
WangRAlligierRAllignolC, et al. Cooperation of combinatorial solvers for en-route conflict resolution. Transport Res Part C Emerg Technol2020; 114: 36–58.
12.
TorataniD.Application of merging optimization to an arrival manager algorithm considering trajectory-based operations. Transport Res Part C Emerg Technol2019; 109: 40–59.
13.
Alonso-AyusoAEscuderoLFMartín-CampoFJ.Exact and approximate solving of the aircraft collision resolution problem via turn changes. Transport Sci2016; 50: 263–274.
14.
CafieriSOmheniR.Mixed-integer nonlinear programming for aircraft conflict avoidance sequentially applying velocity and heading angle changes. Eur J Oper Res2017; 260: 283–290.
15.
CaiJLZhangN.Mixed integer nonlinear programming for aircraft conflict avoidance by applying velocity and altitude changes. Arab J Sci Eng2019; 44: 8893–8903.
16.
GuanXZhangXHanD, et al. A strategic flight conflict avoidance approach based on a memetic algorithm. Chin J Aeronaut2014; 27: 93–101.
17.
LevesonN.A systems approach to risk management through leading safety indicators. Reliabil Eng Syst Saf2015; 136: 17–34.
18.
Niu HT, Ma CB, Wang C, et al. Hazard analysis of traffic collision avoidance system based on STAMP model. In: IEEE International Conference on Progress in Informatics and Computing, Suzhou, China, 14--16 December 2018. Suzhou: IEEE.
19.
KucharJKYangLC.A review of conflict detection and resolution modeling methods. IEEE Trans Intell Transport Syst2000; 1: 179–189.
20.
StephaneMSheilaC. An airborne conflict resolution approach using a genetic algorithm. In: AIAA guidance, navigation and control conference and exhibit, Montreal, Canada, 6–9 August 2001. Montreal: AIAA.
21.
KhatibO.Real-time obstacle avoidance for manipulators and mobile robots. Int J Robot Res1986; 5: 90–98.
22.
BlomHAPBakkerGJ.Safety evaluation of advanced self-separation under very high en route traffic demand. J Aerosp Inform Syst2015; 12: 413–415.
23.
BlomHAPBakkerGJ. Agent-based modeling and simulation of trajectory based operations under very high traffic demand. In: Proceedings of the 6th SESAR innovation days, Netherlands, 2016.
PaoloFZviS. Motion planning in dynamic environments using the relative velocity paradigm. In: IEEE International conference on robotics and automation, Atlanta, USA, 2–6 May 1993, pp. 560. 565. Atlanta: IEEE.
29.
Den Berg JV, Snape J, Guy SJ, et al. Reciprocal collision avoidance with acceleration-velocity obstacles. In: IEEE International conference on robotics and automation, Shanghai, China, 9–13 May 2011, pp. 3475–3482. Shanghai: IEEE.
30.
Den Berg JV, Guy SJ, Lin M, et al. Reciprocal n-body collision avoidance. Springer Tracts Adv Robot2011; 70: 3–19.
31.
TanCYHuangSTanKK, et al. Three-dimensional collision avoidance for multi unmanned aerial vehicles using velocity obstacle. J Intell Robot Syst2020; 97: 227–248.
32.
HuangYMVan GelderPWenYQ.Velocity obstacle algorithms for collision prevention at sea. Ocean Eng2018; 151: 308–321.
33.
ZhongXYZhongXGPengXF.Velocity-change-space-based dynamic motion planning for mobile robots navigation. Neurocomputing2014; 143: 153–163.
34.
BaldiTLScheggiSAggraviM, et al. Haptic guidance in dynamic environments using optimal reciprocal collision avoidance. IEEE Robot Autom Lett2018; 3: 265–272.
35.
DurandN.Constant speed optimal reciprocal collision avoidance. Transport Res Part C Emerg Technol2018; 96: 366–379.
36.
Aerospace forecast fiscal years 2015–2035. Report, Federal Aviation Administration, USA, August 2014.
37.
TellerTMakiEOlsonW, et al. November 2014 proof-of-concept ACAS-Xu flight test. Air Traffic Control Q2015; 23: 183–201.
38.
ErevIBarronGRemingtonR.Right of way in the sky: two problems in aircraft self-separation and the auction-based solution. Hum Factors2004; 46: 267–276.
39.
JenieYIKampenEJVDe VisserCC, et al. Selective velocity obstacle method for deconflicting maneuvers applied to unmanned aerial vehicles. J Guid Control Dyn2015; 38: 1140–1146.
40.
Right-of-way rules: except water operations. Report, Air traffic and general operating rules, Federal Aviation Administration, USA, July 2004.
41.
BoniniDDupréCGrangerG. How Erasmus can support an increase in capacity in 2020. In: Proceedings of the 7th international conference on computing, communications and control technologies: CCCT 2009, Orlando, FL, 2009.
42.
Calvo-FernándezEPerez-SanzLCordero-GarcíaJM, et al. Conflict-free trajectory planning based on a data-driven conflict resolution model. J Guid Control Dynam2017; 40: 615–627.
