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Multi-helicopter collaborative search and rescue operation research based on decision-making

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Abstract

The aim of this paper was to analyse and model the behaviour decision-making of rescue crews in multi-helicopter collaborative search and rescue mission. Firstly, based on decision field theory, a dynamic behavioural decision-making model was put forward considering personal behaviour decision-making preference. Besides, considering physical characteristics, safety requirements and rescue crews’ behaviour decision-making, a multi-helicopter collaborative search and rescue behaviour model was established. Then, based on the survey of four general aviation helicopter search and rescue companies, the search and rescue efficiency by teams composed of different decision-making preferences was simulated based on distributed ant colony algorithm in experiments. Results showed that rescue crews with different personal preferences have different behaviour characteristics. Besides, teams composed of mixed preferences are more efficient than teams composed of single preferences, and the most optimal composition way is when the positive type is slightly more than the conservative type and balanced type.

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References

  1. Abdelgader AMS, Wu L, Nasr MMM (2016) A simplified mobile ad hoc network structure for helicopter communication. Int J Aerosp Eng. https://doi.org/10.1155/2016/2132941

    Article  Google Scholar 

  2. Andruszkow H, Schweigkofler U, Lefering R, Frey M, Horst K, Pfeifer R et al (2016) Impact of helicopter emergency medical service in traumatized patients: which patient benefits most? PLoS ONE 11(1):e0146897. https://doi.org/10.1371/journal.pone.0146897

    Article  Google Scholar 

  3. Gondaliya N, Kathiriya D (2014) An application of ad hoc networks in disaster area for search and rescue operation: a survery and challenges. Int J Adv Res Comput Commun Eng 3(3):5711–5714

    Google Scholar 

  4. Okuno Y, Kobayashi K, Ishii H (2016) Development of a helicopter operations management system for disaster relief missions. J Am Helicopter Soc 61(1):1–9. https://doi.org/10.4050/JAHS.61.012006

    Article  Google Scholar 

  5. Toda M (1962) The design of a fungus-eater: a model of human behaviour in an unsophisticated environment. Behav Sci 7(2):164–183. https://doi.org/10.1002/bs.3830070203

    Article  Google Scholar 

  6. Brehmer B, Allard R (1991) Dynamic decision making: the effects of task complexity and feedback delay. In: Rasmussen J, Brehmer B, Leplat J (eds) New technologies and work. Distributed decision making: cognitive models for cooperative work. Wiley, Oxford, pp 319–334

    Google Scholar 

  7. Anzai Y (1984) Cognitive control of real-time event-driven systems. Cogn Sci 8(3):221–254. https://doi.org/10.1016/S0364-0213(84)80002-6

    Article  Google Scholar 

  8. Kahneman D, Tversky A (2013) Prospect theory: an analysis of decision under risk. Handb Fundam Financ Decis Mak Part I. https://doi.org/10.1142/9789814417358_0006

    Article  MATH  Google Scholar 

  9. Busemeyer JR, Townsend JT (1993) Decision field theory: a dynamic-cognitive approach to decision making in an uncertain environment. Psychol Rev 100(3):432

    Article  Google Scholar 

  10. Gonzalez C, Lerch JF, Lebiere C (2003) Instance-based learning in dynamic decision making. Cogn Sci 27(4):591–635. https://doi.org/10.1016/S0364-0213(03)00031-4

    Article  Google Scholar 

  11. Wang Z, Liang L (1998) A study on dynamic framing effects in risky decision. Acta Psychol Sin 30(04):394–400

    Google Scholar 

  12. Gray R, Franci A, Srivastava V et al (2018) Multi-agent decision-making dynamics inspired by honeybees. IEEE Trans Control Netw Syst 5(2):793–806. https://doi.org/10.1109/TCNS.2018.2796301

    Article  MathSciNet  MATH  Google Scholar 

  13. Valente J, Barrientos A, Del Cerro J, Rossi C, Colorado J, Sanz D et al (2011) Multi-robot visual coverage path planning: geometrical metamorphosis of the workspace through raster graphics based approaches. In: International Conference on Computational Science and Its Applications. Springer, Berlin, pp 58–73. https://doi.org/10.1007/978-3-642-21931-3_5

  14. Rasche C, Stern C, Kleinjohann L, Kleinjohann B (2011) A distributed multi-UAV path planning approach for 3D environments. In: 2011 5th International Conference on Automation, Robotics and Applications (ICARA). IEEE, pp 7–12. https://doi.org/10.1109/ICARA.2011.6144847

  15. Peng H, Li Y, Wang L, Shen L (2008) Hormone-inspired cooperative control for multiple UAVs wide area search. In: Advanced Intelligent Computing Theories and Applications, with Aspects of Theoretical and Methodological Issues, pp 808–816. https://doi.org/10.1007/978-3-540-87442-3_99

  16. Galceran E, Carreras M (2013) A survey on coverage path planning for robotics. Robot Auton Syst 61(12):1258–1276. https://doi.org/10.1016/j.robot.2013.09.004

    Article  Google Scholar 

  17. Chen Y (2007) Ant colony optimization theory applied to UAV tactical control problem. PhD thesis, National University of Defense Technology, Changsha

  18. Beck Z, Teacy WTL, Rogers A et al (2018) Collaborative online planning for automated victim search in disaster response. Robot Auton Syst 100:251–266. https://doi.org/10.1016/j.robot.2017.09.014

    Article  Google Scholar 

  19. Yanmaz E, Yahyanejad S, Rinner B et al (2018) Drone networks: communications, coordination, and sensing. Ad Hoc Netw 68:1–15. https://doi.org/10.1016/j.adhoc.2017.09.001

    Article  Google Scholar 

  20. Roe RM, Busemeyer JR, Townsend JT (2001) Multialternative decision field theory: a dynamic connectionst model of decision making. Psychol Rev 108(2):370. https://doi.org/10.1037/0033-295X.108.2.370

    Article  Google Scholar 

  21. Gao F (2013) Study on methods and applications for process-oriented dynamic decision making. PhD thesis, Huazhong University of Science and Technology, Wuhan

  22. Ju F (2007) Investigation of airplane cockpit design based on ergonomics. PhD thesis, Northwestern Polytechnical University, Xi’an

  23. Busemer JR, Diederich A (2002) Survey of decision field theory. Math Soc Sci 43:345–370. https://doi.org/10.1016/S0165-4896(02)00016-1

    Article  MathSciNet  Google Scholar 

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Acknowledgements

The authors acknowledge the National Key R&D Program of China (No.2018YFC0809500) and National Natural Science Foundation of China (Grant No.71573122 and No.71874081).

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Authors and Affiliations

  1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Quan Shao, Meng Jia & Chenchen Xu

  2. Commercial Aircraft Corporation of China Ltd, Shanghai, China

    Yan Zhu

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  1. Quan Shao
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  2. Meng Jia
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  3. Chenchen Xu
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  4. Yan Zhu
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Correspondence to Quan Shao.

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Shao, Q., Jia, M., Xu, C. et al. Multi-helicopter collaborative search and rescue operation research based on decision-making. J Supercomput 76, 3231–3251 (2020). https://doi.org/10.1007/s11227-018-2555-7

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