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Consensus-based bundle algorithm with local replanning for heterogeneous multi-UAV system in the time-sensitive and dynamic environment

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Abstract

Consensus-based bundle algorithm (CBBA) is a decentralized task allocation algorithm that can produce feasible and conflict-free task assignment solution for multi-UAV system in the search and rescue scenarios. Further considering the new emerging tasks, this paper studies how to realize task reasssignment in the time-sensitive and dynamic environment. Effective task replanning algorithm aims to maximize the score value of task replanning solution when ensuring the timely allocation of the new task. Thus, an extension of CBBA called CBBA with local replanning (CBBA-LR) is proposed to produce reliable task replanning solution with quick response to the new task. Firstly, the capable matrix is adopted in CBBA-LR to denote the capable relationship between UAVs and tasks. Only capable UAVs for the new task are included in the task replanning. Then, the performing time list is introduced to the bid lists. For each UAV, CBBA-LR selects the assigned tasks whose performing times overlap the time window of the new task as the potential reset tasks. The setting of potential reset tasks effectively reduces the number of tasks included in the replanning process. After that, each UAV selects the nearest task to the new task from the potential reset task set as the reset task. Hence, CBBA-LR resets the most likely insert position of the new task from each UAV’s path. Finally, CBBA runs based on the reset task schedules to get the task replanning solution. Numerical simulations demonstrate the solution quality and convergence time of CBBA-LR from four perspectives: different time windows of the new task, different locations of the new task, continuous appearance of new tasks and different scales of search and rescue scenarios. The simulation results verify the feasibility and superiority of CBBA-LR compared with other replanning strategies.

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References

  1. Kurdi H, AlDaood MF, Al-Megren S et al (2019) Adaptive task allocation for multi-UAV systems based on bacteria foraging behaviour. Appl Soft Comput 83:105643. https://doi.org/10.1016/j.asoc.2019.105643

    Article  Google Scholar 

  2. Zhang Y, Feng W, Shi G et al (2020) UAV swarm mission planning in dynamic environment using consensus-based bundle algorithm. Sensors 20(8):2307. https://doi.org/10.3390/s20082307

    Article  Google Scholar 

  3. Bozek P, Ivandic Z, Lozhkin A et al (2016) Solutions to the characteristic equation for industrial robot’s elliptic trajectories. Tehnicki Vjesnik 23(4):1017–1023

    Google Scholar 

  4. Bozek P, Lozhkin A (2019) The precision calculating method of robots moving by the plane trajectories. Int J Adv Robot Syst 16(6):1729881419889556. https://doi.org/10.1177/1729881419889556

    Article  Google Scholar 

  5. Li Y, Jiu M, Sun Q et al (2019) An adaptive distributed consensus control algorithm based on continuous terminal sliding model for multiple quad rotors’ formation tracking. IEEE Access 7:173955–173967. https://doi.org/10.1109/access.2019.2956962

    Article  Google Scholar 

  6. Talebpour Z, Martinoli A (2019) Adaptive risk-based replanning for human-aware multi-robot task allocation with local perception. IEEE Robot Autom Lett 4(4):3790–3797. https://doi.org/10.1109/lra.2019.2926966

    Article  Google Scholar 

  7. Chen Y, Yang D, Yu J (2018) Multi-UAV task assignment with parameter and time-sensitive uncertainties using modified two-part wolf pack search algorithm. IEEE Trans Aerosp Electron Syst 54(6):2853–2872. https://doi.org/10.1109/taes.2018.2831138

    Article  Google Scholar 

  8. Omidshafiei S, Agha-Mohammadi AA, Amato C et al (2017) Decentralized control of multi-robot partially observable Markov decision processes using belief space macro-actions. Int J Robot Res 36(2):231–258. https://doi.org/10.1177/0278364917692864

    Article  Google Scholar 

  9. Shima T, Rasmussen SJ, Sparks AG et al (2006) Multiple task assignments for cooperating uninhabited aerial vehicles using genetic algorithms. Comput Op Res 33(11):3252–3269. https://doi.org/10.1016/j.cor.2005.02.039

    Article  MATH  Google Scholar 

  10. Garcia P, Caamano P, Duro RJ et al (2013) Scalable task assignment for heterogeneous multi-robot teams. Int J Adv Robot Syst 10(2):105. https://doi.org/10.5772/55489

    Article  Google Scholar 

  11. 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. https://doi.org/10.1007/s10846-017-0493-x

    Article  Google Scholar 

  12. Kim M, Morrison JR (2019) On systems of UAVs for persistent security presence: a generic network representation, MDP formulation and heuristics for task allocation. 2019 IEEE International Conference on Unmanned Aircraft Systems (ICUAS), Atlanta, GA, USA, 238-245. https://doi.org/10.1109/icuas.2019.8797863

  13. Gan W, Zhu D, Ji D (2018) QPSO-model predictive control-based approach to dynamic trajectory tracking control for unmanned underwater vehicles. Ocean Eng 158:208–220. https://doi.org/10.1016/j.oceaneng.2018.03.078

    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. https://doi.org/10.1109/tro.2009.2022423

    Article  Google Scholar 

  15. Johnson L, Ponda S, Choi HL et al (2010) Improving the efficiency of a decentralized tasking algorithm for UAV teams with asynchronous communications. AIAA Guidance, Navigation, and Control Conference, Toronto, Ontario, Canada 8421. https://doi.org/10.2514/6.2010-8421

  16. Rantanen M, Modares J, Mastronarde N, et al. (2018) Performance of the asynchronous consensus based bundle algorithm in lossy network environments. 2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop (SAM), Sheffield, UK, 311-315. https://doi.org/10.1109/sam.2018.8448984

  17. Kim KS, Kim HY, Choi HL (2020) A bid-based grouping method for communication-efficient decentralized multi-UAV task allocation. Int J Aero Space Sci 21(1):290–302. https://doi.org/10.1007/s42405-019-00205-1

    Article  Google Scholar 

  18. Kim KS, Kim HY, Choi HL (2019) Minimizing communications in decentralized greedy task allocation. J Aero Inform Syst 16(8):340–345. https://doi.org/10.2514/1.i010624

    Article  Google Scholar 

  19. Samiei A, Ismail S, Sun L, (2019) Cluster-based hungarian approach to task allocation for unmanned aerial vehicles. (2019) IEEE National Aerospace and Electronics Conference (NAECON). IEEE, Dayton, OH, USA 148–154. https://doi.org/10.1109/naecon46414.2019.9057847

  20. Zitouni F, Harous S, Maamri R (2020) A distributed approach to the multi-robot task allocation problem using the consensus-based bundle algorithm and ant colony system. IEEE Access 8:27479–27494. https://doi.org/10.1109/access.2020.2971585

    Article  Google Scholar 

  21. Binetti G, Naso D, Turchiano B (2013) Decentralized task allocation for surveillance systems with critical tasks. Robot Autonom Syst 61(12):1653–1664. https://doi.org/10.1016/j.robot.2013.06.007

    Article  Google Scholar 

  22. Fu X, Feng P, Li B et al (2019) A two-layer task assignment algorithm for UAV swarm based on feature weight clustering. Int J Aerosp Eng 3504248:1–12. https://doi.org/10.1155/2019/3504248

    Article  Google Scholar 

  23. 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. https://doi.org/10.1007/s12559-014-9265-0

    Article  Google Scholar 

  24. Nunes E, Manner M, Mitiche H et al (2017) A taxonomy for task allocation problems with temporal and ordering constraints. Robot Auton Syst 90:55–70. https://doi.org/10.1016/j.robot.2016.10.008

    Article  Google Scholar 

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

    Article  Google Scholar 

  26. Buckman N, Choi HL, How JP, (2019) Partial replanning for decentralized dynamic task allocation. AIAA Scitech, (2019) Forum. San Diego, California, USA

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Acknowledgements

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

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Authors and Affiliations

  1. Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu, 610213, China

    Jie Chen, Xianguo Qing, Kai Xiao & Kai You

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

    Fang Ye & Qian Sun

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Correspondence to Jie Chen.

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Chen, J., Qing, X., Ye, F. et al. Consensus-based bundle algorithm with local replanning for heterogeneous multi-UAV system in the time-sensitive and dynamic environment. J Supercomput 78, 1712–1740 (2022). https://doi.org/10.1007/s11227-021-03940-z

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