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Velocity obstacle–based conflict resolution and recovery method

Published online by Cambridge University Press:  09 August 2021

F. Sun ,
Y. Chen Open the ORCID record for Y. Chen [Opens in a new window] ,
X. Xu ,
Y. Mu  and
Z. Wang
F. Sun
Affiliation:
Jiangning Road Campus Nanjing University of Aeronautics and Astronautics Jiangning District Nanjing CityJiangsu ProvinceChina
Y. Chen*
Affiliation:
Jiangning Road Campus Nanjing University of Aeronautics and Astronautics Jiangning District Nanjing CityJiangsu ProvinceChina
X. Xu
Affiliation:
Jiangning Road Campus Nanjing University of Aeronautics and Astronautics Jiangning District Nanjing CityJiangsu ProvinceChina
Y. Mu
Affiliation:
Jiangning Road Campus Nanjing University of Aeronautics and Astronautics Jiangning District Nanjing CityJiangsu ProvinceChina
Z. Wang
Affiliation:
No.1 Jiazi Changle East Road Xi ’anShanxiChina
*
chengyujun7425@nuaa.edu.cn
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Abstract

Considering the shortcomings of current methods for real-time resolution of two-aircraft flight conflicts, a geometric optimal conflict resolution and recovery method based on the velocity obstacle method for two aircraft and a cooperative conflict resolution method for multiple aircraft are proposed. The conflict type was determined according to the relative position and velocity of the aircraft, and a corresponding conflict mitigation strategy was selected. A resolution manoeuvre and a recovery manoeuvre were performed. On the basis of a two-aircraft conflict resolution model, a multi-aircraft cooperative conflict resolution game was constructed to identify an optimal solution for maximising group welfare. The solution and recovery method is simple and effective, and no new flight conflicts are introduced during track recovery. For multi-aircraft conflict resolution, an equilibrium point that maximises the welfare function of the group was identified, and thus, an optimal strategy for multi-aircraft conflict resolution was obtained.

Keywords

collision detection and resolutioncooperative gamegeometrical algorithmoptimal solution of alliance welfareparticle swarm optimisation algorithmrecovery trackvelocity obstacle method
Type
Research Article
Information
The Aeronautical Journal , Volume 126 , Issue 1296 , February 2022 , pp. 345 - 364
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Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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References

REFERENCES

Kuchar, J.K. and Yang, L.C. A review of conflict detection and resolution modeling methods, IEEE Trans Intell Transport Syst, 2000, 1, (4), pp 179–189.CrossRefGoogle Scholar
Zhang, J.Y., Zhao, Z.P. and Liu, T. A path planning method for mobile robot based on artificial potential field, J Harb Instit Technol, 2006, 38, (8), pp 1306–1309.Google Scholar
Zhang, J.Y. and Liu, T. Optimized path planning of mobile robot based on artificial potential field, Acta Aeronauticaet Astronautica Sinica, 2007, 28, (S1), pp 183–188.Google Scholar
Liu, X., Hu, M.H. and Dong, X.N. Application of genetic algorithms for solving flight conflicts, J Nanjing Univ Aeronaut Astronaut, 2002, 34, (1), pp 35–39.Google Scholar
Delahaye, D., Peyronne, C., Mongeau, M. and Puechmorel, S. Aircraft conflict resolution by genetic algorithm and B-spline approximation, Proceedings of the 2nd ENRI international workshop on ATM/CNS, Tokyo, Japan, 2010 November 10–12, pp 71–78.Google Scholar
Chen, L.Y., Han, S.C. and Liu, X. Optimal conflict resolution method based on inner-point restriction, J Traff Transp Eng, 2005, 5, (2), pp 80–84.Google Scholar
Han, Y.X., Tang, X.M. and Han, S.C. Conflict resolution model of optimal flight for fixation airway, J Traff Transp Eng, 2012, 12, (1), pp 115–120.Google Scholar
Tang, X.M., Chen, P. and Li, B. Optimal air route flight conflict resolution based on receding horizon control, Aerosp Sci Technol, 2016, 50, pp 77–87.CrossRefGoogle Scholar
Guan, X.M. and Lyu, R.L. Aircraft conflict resolution method based on satisfying game theory, Acta Aeronauticaet Astronautica Sinica, 2017, 38, (S1), p 721475.Google Scholar
Xu, K., Yin, H., Zhang, L. and Xu, Y. Game theory with probabilistic prediction for conflict resolution in air traffic management, 2015 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), Taipei, Taiwan, 2015 November, pp 94–98.CrossRefGoogle Scholar
Zhu, C.Y. and Meng, X. Automatic obstacle avoidance algorithm for UAV in dynamic uncertain environment, J Wuhan Univ Technol (Transp Sci Eng), 2013, 37, (2), pp 307–310.Google Scholar
Billimoria Karl, D. A geometric optimization approach to aircraft conflict resolution, AIAA Guidance, Navigation, and Control Conference and Exhibit, Reston, VA, AIAA, 2000, pp 14–17.Google Scholar
Bilimoria, K.D., Sridhar, B., Chatterji, G.B., et al. Facet: Future ATM concepts evaluation tool, Proceedings of the 3rd USA/Europe ATM 2001 R&D Seminar, 2000, p 9.CrossRefGoogle Scholar
Hwang, I., Kim, J. and Tomlin, C. Protocol-based conflict resolution for air traffic control, ATC Qtly, 2007, 15, (1), pp 1–34.Google Scholar
Zhang, Y., Zhang, M. and Yu, J. Real-time flight conflict detection and release based on multi-agent system, IOP Conference Series: Earth and Environmental Science, 2018, 108, (3), p 032053.CrossRefGoogle Scholar
Geser, A. and Munoz, C. A geometric approach to strategic conflict detection and resolution, The 21st Digital Avionics Systems Conference, Irvine, CA, USA, 27–31 October 2002, pp. 6B1-6B1.Google Scholar
Omer, J. A space-discretized mixed-integer linear model for air-conflict resolution with speed and heading maneuvers, Comp Oper Res, 2015, 58, pp 75–86.CrossRefGoogle Scholar
Goss, J., Rajvanshi, R. and Subbarao, K. Aircraft conflict detection and resolution using mixed geometric and collision cone approaches, AIAA 2004-4879. AIAA Guidance, Navigation, and Control Conference and Exhibit, Providence, Rhode Island, 16–19 August 2004.CrossRefGoogle Scholar
Mueller, T., Schleicher, D. and Bilimoria, K. Conflict detection and resolution with traffic flow constraints, AIAA 2002-4445. AIAA Guidance, Navigation, and Control Conference and Exhibit, Monterey, California, 5–8 August 2002.CrossRefGoogle Scholar
Li, X., Xu, X.H. and Zhu, C.Y. Air traffic reroute planning based on geometry algorithm. Syst Eng, 2008, 26, (8), pp 37–40.Google Scholar
Zhang, M., Yu, J., Zahng, Y., Wang, S. and Yu, H. Flight conflict resolution during low-altitude rescue operation based on ensemble conflict models, Adv Mechan Eng, 2017, 9, (4), p 88–98.Google Scholar
Van Den Berg, J., Lin, M.C. and Manocha, D. Reciprocal velocity obstacles for real-time multi-agent navigation, 2008 IEEE International Conference on Robotics and Automation, ICRA 2008, Pasadena, California, USA, IEEE, 19–23 May 2008, pp 1928–1935.CrossRefGoogle Scholar
Van Den Berg, J., Guy, S.J., Lin, M. and Manocha, D. Robotics Research. Springer Tracts in Advanced Robotics, Berlin, Heidelberg: Springer, 2009, pp 3–19.Google Scholar
Durand, N. and Barnier, N. Does ATM need centralized coordination? Autonomous conflict resolution analysis in a constrained speed environment, Air Traffic Control Q, 2015, 23, (4), pp 325–346.CrossRefGoogle Scholar
Allignol, C., Barnier, N., Durand, N., Manfredi, G. and Blond, E. Assessing the robustness of a UAS detect & avoid algorithm, 12th USA/Europe Air Traffic Management Research and Development Seminar, Seattle, CA, USA, June 2017.Google Scholar
Allignol, C., Barnier, N., Durand, N., Manfredi, G. and Blond, E. Integration of UAS in terminal control area, 2016 IEEE/AIAA 35th Digital Avionics Systems Conference (DASC), Sacramento, CA, 12 December 2016, pp 1–7.CrossRefGoogle Scholar
Yang, X.X., Zhou, W.W. and Zhang, Y. Automatic obstacle-avoidance planning for UAV based on velocity obstacle arc method, Syst Eng Electr, 2017, 39, (11), pp 618–176.Google Scholar
Yang, X.X., Zhang, Y. and Zhou, W.W. Automatic obstacle avoidance algorithm for UAV in dynamic uncertain environment, Syst Eng Electr, 2017, 39, (11), pp 2546–2552.Google Scholar
Wang, L.L. and Yang, H.D. Rerouting strategy research based on geometry algorithm in flight conflict, Flight Dynam, 2012, 30, (5), pp 466–469.Google Scholar
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