Skip to main content
Log in

Research on uncertain bi-objective UAV mission allocation problem

  • Special Issue
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

Based on the uncertainty theory, this paper studies the uncertain bi-objective UAV mission allocation problem in uncertain environment. Firstly, by regarding uncertainty factors in the mission allocation planning as uncertain variables and considering two missions of combat mission gains and flight fuel consumption, a uncertain bi-objective UAV mission allocation (UBUMA) model is established. Secondly, in order to overcome the disconnection between the objective functions caused by the traditional method to deal with uncertain factors, this paper proposes a so-called uncertain method to solve UBMUA problem by defining the relationship of order between uncertain variables. According the real decision-making process, the UBUMA is transformed into a single-objective programming problem by using \(C_E\) principle relation. Finally, the ant algorithm is employed to solve the single-objective programming problem and then the \(C_E\) efficient mission routes are obtained. The simulation results show that this method can effectively deal with UBUMA problem, and the mission allocation efficient routes is reasonable.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Alotaibi ET, Saleh AS et al (2019) LSAR: multi-UAV collaboration for search and rescue missions. IEEE Access 7:55817–55832

    Article  Google Scholar 

  2. Chen X, Tang T (2013) Study on the dynamic task assignment of multi-UCAV under dynamic uncertain environment. Fire Control Command Control 38(1):45–53

    Google Scholar 

  3. Cristian RA, David C (2019) Constrained multi-objective optimization for multi-UAV planning. Decis Control 10(6):2479–2496

    Google Scholar 

  4. Fu Z, Mao Y et al (2020) Secure multi-UAV collaborative task allocation. IEEE Access 7:35579–35587

    Article  Google Scholar 

  5. Gao J, Yao K (2015) Some concepts and theorems of uncertain random process. Int J Intell Syst 30(1):52–65

    Article  Google Scholar 

  6. Gong Z, Wang H (2020) Measuring trust in social networks based on linear uncertainty theory. Inf Sci 508:154–172

    Article  MathSciNet  Google Scholar 

  7. Liu B (2007) Uncertainty theory, 2nd edn. Springer, Berlin

    Google Scholar 

  8. Liu B (2009) Some research problems in uncertainty theory. J Uncert Syst 3(1):3–10

    Google Scholar 

  9. Liu B (2012) Why is there a need for uncertainty theory. J Uncert Syst 6(1):3–10

    MathSciNet  CAS  Google Scholar 

  10. Wang X, Xu J, Zheng M et al (2015) Aviation risk analysis: U-bowtie model based on chance theory. IEEE Access 7:86664–86677

    Article  Google Scholar 

  11. Wang Z, Guo J, Chen J, Tian S (2019a) Uncertain team orienteering problem with time windows based on uncertainty theory. IEEE Access 7:63403–63414

    Article  Google Scholar 

  12. Wang Z, Guo J, Zheng M (2019b) Research on a novel minimum-risk model for uncertain orienteering problem based on uncertainty theory. Soft Comput 23(12):4573–4584

    Article  Google Scholar 

  13. Yao K, Liu B (2018) Uncertain regression analysis: an approach for imprecise observations. Soft Comput 22(17):5579–5582

    Article  Google Scholar 

  14. Yao K, Gao J, Gao Y (2013) Some stability theorems of uncertain differential equation. Fuzzy Optim Decis Making 12(1):3–13

    Article  MathSciNet  Google Scholar 

  15. Zhang Y, Hu B et al (2016) Research on unmanned aerial vehicle (UAV) Reconnaissance decision in multi-mission area under uncertain environment. J Northwest Polytech Univ 34(6):1028–1034

    Google Scholar 

  16. Zhang J, Xing J et al (2020) Cooperative task assignment of multi-UAV system. Chin J Aeronaut 33(11):2825–2827

    Article  Google Scholar 

  17. Zheng M, Yi Y (2018) The information value and the uncertainties in two-stage uncertain programming with recourse. Soft Comput 22(17):5791–5801

    Article  Google Scholar 

  18. Zheng M, Yi Y, Wang Z et al (2016) Efficient solution concepts and their application in uncertain multiobjective programming. Appl Soft Comput 56:557–569

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mingfa Zheng.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work was supported by the Natural Science Foundation of Shaanxi Province of China under Grant 2019JM-271.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, L., Zheng, M., Zhong, H. et al. Research on uncertain bi-objective UAV mission allocation problem. Evol. Intel. 17, 229–237 (2024). https://doi.org/10.1007/s12065-021-00670-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-021-00670-2

Keywords

Navigation