Conflict resolution is one of the central tasks during the control of air traffic. In this article, we examined the problem of conflict resolution for unmanned aerial vehicle (UAV) integration into national airspace system and presented an approach based on satisficing game theory to conflict resolution for cooperative UAVs and manned aircraft sharing airspace. This approach ensured the priority of manned aircraft to alleviate the impact of UAVs on air traffic control and reduced the resistance of UAVs integrating into the controlled airspace. At the same time, the approach took account of the heterogeneity of aircraft, which is more realistic considering the UAVs and manned aircraft sharing the same airspace. The preliminary result of the simulation proved that our method was qualified to tackle many tough scenarios.
Get full access to this article
View all access options for this article.
References
1.
JenieYI, van KampenEJ, EllerbroekJ, HoekstraJM. Taxonomy of conflict detection and resolution approaches for unmanned aerial vehicle in an integrated airspace. IEEE Trans Intell Transp Syst. 2017; 18:558–567.
2.
GoelS, KealyA, LohaniB. Development and experimental evaluation of a low-cost cooperative UAV localization network prototype. J Sens Actuator Netw. 2018; 7:42.
3.
RayPP, DashD, KumarN. Sensors for internet of medical things: State-of-the-art, security and privacy issues, challenges and future directions. Comput Commun. 2020; 160:111–131.
4.
Lopez-FrancoC, Gomez-AvilaJ, AlanisAY, et al.Visual servoing for an autonomous hexarotor using a neural network based PID controller. Sensors. 2017; 17:1865.
5.
AggarwalS, KumarN. Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges. Comput Commun. 2020; 149:270–299.
6.
KhosiawanY, ParkY, MoonI, et al.Task scheduling system for UAV operations in indoor environment. Neural Comput Appl. 2019; 31:5431–5459.
7.
HoF, GeraldesR, GonçalvesA, et al.Improved conflict detection and resolution for service UAVs in shared airspace. IEEE Trans Veh Technol. 2018; 68:1231–1242.
8.
Li-yuanC, Song-chenH, XingL. Optimal conflict resolution method based on inner-point restriction. J Traffic Transp Eng. 2005; 2:019.
9.
ChengQ, WangX, YangJ, ShenL. Automated enemy avoidance of unmanned aerial vehicles based on reinforcement learning. Appl Sci. 2019; 9:669.
10.
HanS, ChengL, TongH. Noncooperative conflict resolution using differential game. In: 1st International Symposium on Systems and Control in Aerospace andAstronautics. Harbin, China: IEEE. 2006.
11.
YuT, TangJ, BaiL, LaoS. Collision avoidance for cooperative UAVs with rolling optimization algorithm based on predictive state space. Appl Sci. 2017; 7:329.
12.
KucharJK, YangLC. A review of conflict detection and resolution modeling methods. IEEE Trans Intell Transp Syst. 2000; 1:179–189.
13.
Aeronautics RTCf. Final report of RTCA task force 3, free flight implementation. In: International Conference on NLP. Washington, DC: Radio Technical Commission for Aeronautics, 1995.
14.
HillJC, JohnsonFR, ArchibaldJK, et al.A cooperative multi-agent approach to free flight. In: International Joint Conference on Autonomous Agents and Multiagent Systems. New York, NY: Association for Computing Machinery, 2005.
15.
ChaloulosG, LygerosJ, RoussosI, et al.Comparative study of conflict resolution methods. In: iFly Project. The Netherlands: National Aerospace Laboratory, 2009.
16.
BellomiF, BonatoR, NanniV, TedeschiA. Satisficing Game Theory for distributed conflict resolution and traffic optimisation: A simulation tool and experimental results. In: Eurocontrol Innovative Research Workshop. 2007.
17.
ArchibaldJK, HillJC, JepsenNA, et al.A satisficing approach to aircraft conflict resolution. IEEE Trans Syst Man Cybern Part C Appl Rev. 2008; 38:510–521.
18.
JiX, ZhangX, GuanX. A collision avoidance method based on satisfying game theory. In: International Conference on Intelligent Human-Machine Systems and Cybernetics. Nanchang, China: Institute of Electrical and Electronics Engineers (IEEE), 2012.
19.
WuT, DuW.A distributed approach to aircraft conflict resolution based on satisficing game theory. In: Foundations of Intelligent Systems. Berlin, Germany: Springer, 2014, pp. 383–393.
20.
StirlingWC. Satisficing games and decision making. United Kingdom: Cambridge University Press, 2003, pp. 471–489.
21.
MatsunoY, TsuchiyaT, WeiJ, et al.Stochastic optimal control for aircraft conflict resolution under wind uncertainty. Aerosp Sci Technol. 2015; 43:77–88.
22.
TangXM, ChenP, LiB. Optimal air route flight conflict resolution based on receding horizon control. Aerosp Sci Technol. 2015; 50:77–87.
JinXM, HanSC, SunFR. Conflict resolution in free flight with linear programming. J Traffic Transp Eng. 2003; 3:75–79.
25.
KrozelJ, PetersM, BilimoriaK, et al.System performance characteristics of centralized and decentralized air traffic separation strategies. Air Traffic Control Q. 2001; 9.
26.
EmamiH, DerakhshanF. An overview on conflict detection and resolution methods in air traffic management using multi agent systems. In: The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012). Shiraz, Iran, 2012.
27.
Alonso-MoraJ, NaegeliT, SiegwartR, BeardsleyP. Collision avoidance for aerial vehicles in multi-agent scenarios. Auton Robots. 2015; 39:101–121.
28.
BreilR, DelahayeD, LapassetL, FéronE. Multi-agent systems for air traffic conflicts resolution by local speed regulation. In: 2016 IEEE/AIAA 35th Digital Avionics Systems Conference. 2016. pp. 10.
29.
MylvaganamT, SassanoM, AstolfiA. A differential game approach to multi-agent collision avoidance. IEEE Trans Autom Control. 2017; PP(99):1–1.
30.
SimonHA.A behavioral model of rational choice. Q J Econ. 1955; 69:99–118.
31.
LinCE, LeeCJ. Conflict detection and resolution model for low altitude flights. In: 2015 20th International Conference on Methods and Models in Automation and Robotics (MMAR). Miedzyzdroje, Poland: IEEE. 2015. pp. 406–411.
32.
DugailD, FeronE, BilimoriaK. Stability of intersecting aircraft flows using heading change maneuvers for conflict avoidance. In: Proceedings of the 2002 American Control Conference. Anchorage, AK: IEEE. 2002.
