Skip to main content
SpringerLink
Log in

Decentralized task allocation for heterogeneous multi-UAV system with task coupling constraints

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Cooperative multiple task assignment problem is an essential issue in the collaboration of multiple unmanned aerial vehicles (UAVs). Consensus-based bundle algorithm (CBBA) is a decentralized task assignment method that only considers homogeneous agents and independent tasks. Thus, we develop an extended CBBA with task coupling constraints (CBBA-TCC) in this paper to solve the multi-task assignment problem with task coupling constraints in the heterogeneous multi-UAV system. CBBA is a two-stage iteration algorithm with inner and outer consensus stages. The inner consensus stage is designed as a modified version of CBBA in this paper. A Can-do list is firstly raised at the beginning of bundle construction phase on each agent to record the tasks that can be performed by this agent without violating the task precedence constraints. Hence, at the inner consensus stage, each agent will only bid on the Can-do list. Then, we adopt a task performing time list for each agent to store the performing times of its assigned tasks. With associate consensus strategy of task performing time list at the conflict resolution phase, the precedence constraint of coupled tasks can be guaranteed. After reaching inner consensus, the outer consensus stage introduces an insert-position feasibility index to determine whether the assigned tasks satisfy the coupling constraints and resolve the constraint violation conflicts. Through the iterations of inner and outer consensus stages, CBBA will reach global consensus and obtain conflict-free task assignment results. Numerical simulations demonstrate the feasibility and reliability of CBBA in various search and rescue scenarios.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Kurdi HA, Ebtesam A, Maram A et al (2018) Autonomous task allocation for multi-UAV systems based on the locust elastic behavior. Appl Soft Comput 71:110–126

    Article  Google Scholar 

  2. Turner J, Meng Q, Schaefer G et al (2017) Distributed task rescheduling with time constraints for the optimization of total task allocations in a multirobot system. IEEE Trans Cybern 48(9):2583–2597

    Article  Google Scholar 

  3. Dias MB, Zlot R, Kalra N et al (2006) Market-based multirobot coordination: a survey and analysis. Proc IEEE 94(7):1257–1270

    Article  Google Scholar 

  4. Johnson LB, Choi HL, How JP (2016) The role of information assumptions in decentralized task allocation: a tutorial. IEEE Control Syst Mag 36(4):45–58

    Article  MathSciNet  Google Scholar 

  5. Kim MH, Kim SP, Lee S (2012) Social-welfare based task allocation for multi-robot systems with resource constraints. Comput Ind Eng 63(4):994–1002

    Article  Google Scholar 

  6. Trigui S, Koubaa A, Cheikhrouhou O et al (2014) A distributed market-based algorithm for the multi-robot assignment problem. Procedia Comput Sci 32:1108–1114

    Article  Google Scholar 

  7. Edison E, Shima T (2011) Integrated task assignment and path optimization for cooperating uninhabited aerial vehicles using genetic algorithms. Comput Oper Res 38(1):340–356

    Article  MathSciNet  MATH  Google Scholar 

  8. Xu G, Long T, Wang Z et al (2020) Target-bundled genetic algorithm for multi-unmanned aerial vehicle cooperative task assignment considering precedence constraints. Proc Inst Mech Eng Part G J Aerosp Eng 234(3):760–773

    Article  Google Scholar 

  9. Jia Z, Yu J, Ai X et al (2018) Cooperative multiple task assignment problem with stochastic velocities and time windows for heterogeneous unmanned aerial vehicles using a genetic algorithm. Aerosp Sci Technol 76:112–125

    Article  Google Scholar 

  10. Huang H, Zhuo T (2019) Multi-model cooperative task assignment and path planning of multiple UCAV formation. Multimed Tools Appl 78(1):415–436

    Article  Google Scholar 

  11. Zhao W, Meng Q, Chung PWH (2015) A heuristic distributed task allocation method for multivehicle multitask problems and its application to search and rescue scenario. IEEE Trans Cybern 46(4):902–915

    Article  Google Scholar 

  12. Oh G, Kim Y, Ahn J et al (2017) Market-based task assignment for cooperative timing missions in dynamic environments. J Intell Robot Syst 87(1):97–123

    Article  Google Scholar 

  13. Wu W, Cui N, Shan W et al (2018) Distributed task allocation for multiple heterogeneous UAVs based on consensus algorithm and online cooperative strategy. Aircr Eng Aerosp Technol 90(9):1464–1473

    Article  Google Scholar 

  14. Choi HL, Brunet L, How JP (2009) Consensus-based decentralized auctions for robust task allocation. IEEE Trans Robot 25(4):912–926

    Article  Google Scholar 

  15. Choi HL, Whitten AK, How JP (2010) Decentralized task allocation for heterogeneous teams with cooperation constraints. In: Proceedings of the 2010 American Control Conference. IEEE, pp 3057–3062

  16. Bertuccelli L, Choi HL, Cho P et al (2009) Real-time multi-UAV task assignment in dynamic and uncertain environments. In: AIAA Guidance, Navigation, and Control Conference, 5776

  17. Johnson L, Ponda S, Choi HL et al (2010) Improving the efficiency of a decentralized tasking algorithm for UAV teams with asynchronous communications. In: AIAA Guidance, Navigation, and Control Conference, 8421

  18. Johnson L, Ponda S, Choi HL et al (2011) Asynchronous decentralized task allocation for dynamic environments. Infotech Aerospace, San Juan, p 1441

    Google Scholar 

  19. Nayak S, Yeotikar S, Carrillo E et al (2020) Experimental comparison of decentralized task allocation algorithms under imperfect communication. IEEE Robot Autom Lett 5(2):572–579

    Article  Google Scholar 

  20. Buckman N, Choi HL, How JP (2019) Partial replanning for decentralized dynamic task allocation. In: AIAA Scitech 2019 Forum, 0915

  21. Ponda S, Redding J, Choi HL et al (2010) Decentralized planning for complex missions with dynamic communication constraints. In: Proceedings of the 2010 American Control Conference. IEEE, pp 3998–4003

  22. Di Paola D, Naso D, Turchiano B (2011) Consensus-based robust decentralized task assignment for heterogeneous robot networks. In: Proceedings of the 2011 American Control Conference. IEEE, pp 4711–4716

  23. Binetti G, Naso D, Turchiano B (2012) Decentralized task allocation for heterogeneous agent systems with constraints on agent capacity and critical tasks. In: 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, pp 1627–1632

  24. Binetti G, Naso D, Turchiano B (2013) Decentralized task allocation for surveillance systems with critical tasks. Robot Auton Syst 61(12):1653–1664

    Article  Google Scholar 

  25. Hunt S, Meng Q, Hinde C et al (2014) A consensus-based grouping algorithm for multi-agent cooperative task allocation with complex requirements. Cogn Comput 6(3):338–350

    Article  Google Scholar 

  26. Duan X, Liu H, Tang H et al (2019) A novel hybrid auction algorithm for multi-UAVs dynamic task assignment. IEEE Access (early access). https://doi.org/10.1109/ACCESS.2019.2959327

  27. Whitbrook A, Meng Q, Chung PWH (2017) Reliable, distributed scheduling and rescheduling for time-critical, multiagent systems. IEEE Trans Autom Sci Eng 15(2):732–747

    Article  Google Scholar 

  28. Moon S, Oh E, Shim DH (2013) An integral framework of task assignment and path planning for multiple unmanned aerial vehicles in dynamic environments. J Intell Robot Syst 70(1–4):303–313

    Article  Google Scholar 

Download references

Acknowledgements

The paper is funded by the National Natural Science Foundation of China (No. 61701134, No. 51809056), the National Key Research and Development Program of China (No. 2016YFF0102806) and the Natural Science Foundation of Heilongjiang Province, China (No. F2017004).

Author information

Authors and Affiliations

  1. College of Information and Communication Engineering, Harbin Engineering University, Harbin, 150001, China

    Fang Ye, Jie Chen, Qian Sun, Yuan Tian & Tao Jiang

Authors
  1. Fang Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jie Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qian Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuan Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tao Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuan Tian.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ye, F., Chen, J., Sun, Q. et al. Decentralized task allocation for heterogeneous multi-UAV system with task coupling constraints. J Supercomput 77, 111–132 (2021). https://doi.org/10.1007/s11227-020-03264-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-020-03264-4

Keywords

Search

Navigation