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Journal of Combinatorial Optimization

, Volume 37, Issue 1, pp 40–61 | Cite as

A scheduling algorithm for medical emergency rescue aircraft trajectory based on hybrid estimation and intent inference

  • Bin Hu
  • Fang PanEmail author
  • Lei Wang
Article
  • 136 Downloads

Abstract

This paper studies the accurate prediction of the trajectory of aircraft that is in low-lever emergency medical rescue so as to achieve effective and safe real-time scheduling. The paper acquires real-time flight state (position, speed and heading) by taking advantage of the information derived from Automatic Dependence Surveillance-broadcasting and improved A\(^{*}\) algorithm. Then Interacting Multiple Model is introduced to predict the flight state and model of the aircraft at next point-in-time based on the current state information. Then combining the result and flight plan information together, short-term flight intent is inferred. Integrated with Interacting Multiple Model algorithm and improved Intent Inference algorithm, a trajectory prediction algorithm is put forward based on the flight state and intent inference, and finally a real time scheduling plan is coming into being. Case simulation shows the result of this algorithm is more accurate than using intent inference algorithm alone. It helps to guarantee the safety of the medical rescue aircraft and improve the efficiency of the emergency rescue.

Keywords

Low-level medical emergency rescue Trajectory forecast Hybrid estimation algorithm Intent inference 

Notes

Acknowledgements

This paper is partially supported by National Natural Science Foundation of China (No. 61573181), Study on Adaptive Evolution Mechanism of Complicated Low-level Flight Situation and Diversified Coordination Monitor Methodology.

References

  1. Bar-Shalom Y, Li XR (1993) Estimation and tracking: principles, techniques and software. Artech House, LondonzbMATHGoogle Scholar
  2. Burcu A, Ali M (2010) International disaster relief planning with fuzzy credibility. Fuzzy Optim Decis Mak 9(4):413–433MathSciNetCrossRefzbMATHGoogle Scholar
  3. Chatterji GB (1999) Short term trajectory prediction methods. In: AIAA guidance, navigation, and control conference, neuroscience lettersGoogle Scholar
  4. Chen X, Fan ZP, Li ZW, Han XL, Zhang X, Jia HC (2015) A two-stage method for member selection of emergency medical service. J Comb Optim 30:871–891MathSciNetCrossRefzbMATHGoogle Scholar
  5. Chiba R, Arai T, Ota J (2010) Integrated design for automated guided vehicle systems using cooperative co-evolution. Adv Robot 24(1–2):25–45CrossRefGoogle Scholar
  6. Ge BJ (2008) Study on short range regional air flow forecasting. Ph.D. thesis, Nanjing University of Aeronautics and AstronauticsGoogle Scholar
  7. Hwang I, Balakrishnan H, Tomlin C (2006) State estimation for hybrid systems: applications to aircraft tracking. In: IEEE proceedings control theory and applicationsGoogle Scholar
  8. Innocenti M, Gelosi P, Pollini L (1999) Air traffic management using probability function fields. In: Proceedings of the AIAA, guidance, navigation and control conference and exhibit, PortlandGoogle Scholar
  9. Kang L, Zhao CX, Guo JH (2009) Improved path planning based on rapidly-exploring random tree for mobile robot in unknown environment. Patt Recog Artif Intell Patt Recog Artif Intell 22(3):337–343Google Scholar
  10. Khuswendi T, Hindersah H, Adiprawita W (2011) Uav path planning using potential field and modified receding horizon A* 3D algorithm. In: International conference on electrical engineering and informatics (ICEEI), 2011. IEEEGoogle Scholar
  11. Krozel J (2000) Intent inference for free flight aircraft. In: Proceedings of the AIAA guidance, navigation, and control conference and exhibit, DenverGoogle Scholar
  12. Krozel J (2005) Intent inference and strategic path prediction. In: Proceedings of the AIAA guidance, navigation, and control conference and exhibit, San Francisco, CaliforniaGoogle Scholar
  13. Liang Y, Cheng YM (2001) Performance analysis of interactive multiple model algorithm. Control Theor Appl 18(4):487–492Google Scholar
  14. Liu C, Huang HJ, Du HW, Jia XH (2017) Optimal RSUs placement with delay bounded message dissemination in vehicular networks. J Comb Optim 33:1276–1299MathSciNetCrossRefzbMATHGoogle Scholar
  15. Liu SQ, Duan JB, Yu YX (2008) Path planning of uninhabited combat air vehicle based on Voronoi diagram and ant colony optimization algorithm. J Syst Simul 20(21):5936–5939Google Scholar
  16. Liu Y, Yu WY, Liu X, Zhang LL (2016) Modeling the medical rescue forces allocation in earthquakes. In: Lulu Z (ed) Modeling the injury flow and treatment after major earthquakes. Springer, DordrechtGoogle Scholar
  17. Pokorný J, Getífk P, Škach J (1995) Emergency medical services: rescue potential for mass casualties in urban fire disasters. In: Masellis M, Gunn S, William A (eds) The management of burns and fire disasters: perspectives 2000. Springer, NetherlandsGoogle Scholar
  18. Qin L, Xu YF (2017) Fibonacci helps to evacuate from a convex region in a grid network. J Comb Optim 33:1276–1299MathSciNetCrossRefzbMATHGoogle Scholar
  19. Rouse WB, Geddes ND, Curry RE (1987) An architecture for intelligent interfaces: outline of an approach to supporting operators of complex systems. Hum Comput Interact 3(2):87–122CrossRefGoogle Scholar
  20. Shi ZS, Liu Z (2010) Method and theory of target tracking and data fusion. National Defense Industry Press, BeijingGoogle Scholar
  21. Sun DW (2006) Research on TF/TA track planning technology and engineering dissertation. Ph.D. thesis, Northwestern Polytechnical UniversityGoogle Scholar
  22. Vitale U (1992) The use of aircraft in fire disasters: the Italian air force medical corps in burn emergencies. In: Masellis M, Gunn S, William A (eds) The management of mass burn casualties and fire disasters. Kluwer Academic PublishersGoogle Scholar
  23. Wang B, Han XB, Zhang XX, Zhang SH (2015) Predictive-reactive scheduling for single surgical suite subject to random emergency surgery. J Comb Optim 30:949–966MathSciNetCrossRefzbMATHGoogle Scholar
  24. Wang L, Zhang M, Wang S (2014) Low altitude flight path strategy planning method based on 3D airspace grid. Aeronaut Comput Tech 44(3):42–46MathSciNetGoogle Scholar
  25. Wang SJ, Sui D (2010) Risk analysis of flight conflict in low altitude airspace. J Southwest Jiaotong Univ 45(1):116–122Google Scholar
  26. Xie L (2013) Aircraft conflict detection based on 4D trajectory prediction. Ph.D. thesis, Nanjing University of Aeronautics and AstronauticsGoogle Scholar
  27. Xu YF, Zhang HL (2015) How much the grid network and rescuers communication can improve the rescue efficiency in worst-case analysis. J Comb Optim 30:1062–1076MathSciNetCrossRefzbMATHGoogle Scholar
  28. Yang J, Chen JH, Liu HL, Zhang K, Ren W, Zheng JC (2013) The Chinese national emergency medical rescue team response to the Sichuan Lushan earthquake. Nat Hazards 69:2263–2268CrossRefGoogle Scholar
  29. Yang LC, Kuchar JK (1997) Prototype conflict alerting system for free flight. J Guid Control Dyn 20(4):768–773CrossRefzbMATHGoogle Scholar
  30. Yepes JL, Hwang I, Rotea M (2007) New algorithms for aircraft intent inference and trajectory prediction. J Guid Control Dyn 30(2):370–382CrossRefGoogle Scholar
  31. Yu G (2009) Emergency air rescue. Aviation Industry Press, BeijingGoogle Scholar
  32. Zhao WT, Peng JY (2006) Voronoi diagram-based path planning for UAVS. J Syst Simul 18(2):159–162Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.College of Civil AviationNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.School of Healthy Economics and ManagementNanjing University of Chinese MedicineNanjingChina

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