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A hybrid task allocation approach for multi-UAV systems with complex constraints: a market-based bidding strategy and improved NSGA-III optimization

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Abstract

The multiple unmanned aerial vehicle's (multi-UAV's) collaborative task allocation problem with complex constraints has received significant attention in recent years. This paper focuses on the efficient task allocation method for the search and rescue scenario with complex timing and resource constraints. First, the considered scenario is formulated, and a hybrid task allocation method considering complex constraints (HTACC) is proposed by integrating decentralized and distributed algorithm. Specifically, a constraint rule is designed to non-dominated sort all unallocated tasks. And, based on the resource constraints and timing constraints in a distributed manner, a bidding strategy is proposed for each UAV to bid for current task. On this basis, the centralized commander investigates an improved NSGA-III to select a UAV alliance that fulfills the constraints based on the received bids to cooperatively complete the task. Finally, the effectiveness and superiority of the proposed HTACC method are verified through experimental simulations. The results show that HTACC can obtain a better Pareto frontier compared to other algorithms. In addition, HTACC can obtain task schedules within 24.25 s, and the average resource utilization rate is as high as 47.72% in a large-scale scenario of 45 tasks with 100 UAVs.

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References

  1. Alam MM, Moh S (2024) Joint optimization of trajectory control, task offloading, and resource allocation in air-ground integrated networks. IEEE Internet Things J 11(13):24273–24288

    Article  Google Scholar 

  2. An X, Wu C, Lin Y, Lin M, Yoshinaga T, Ji Y (2023) Multi-robot systems and cooperative object transport: communications, platforms, and challenges. IEEE Open J Comput Soc 4:23–36

    Article  MATH  Google Scholar 

  3. Antonyshyn L, Silveira J, Givigi S, Marshall J (2023) Multiple mobile robot task and motion planning: a survey. ACM Comput Surv 55(10):1–35

    Article  Google Scholar 

  4. Baburao D, Pavankumar T, Prabhu C (2023) Load balancing in the fog nodes using particle swarm optimization-based enhanced dynamic resource allocation method. Appl Nanosci 13(2):1045–1054

    Article  Google Scholar 

  5. Bai X, Fielbaum A, Kronmüller M, Knoedler L, Alonso-Mora J (2022) Group-based distributed auction algorithms for multi-robot task assignment. IEEE Trans Autom Sci Eng 20(2):1292–1303

    Article  Google Scholar 

  6. Cao P, Lei L, Cai S, Shen G, Liu X, Wang X, Zhang L, Zhou L, Guizani M (2024) Computational intelligence algorithms for uav swarm networking and collaboration: A comprehensive survey and future directions. IEEE Commun Surv & Tutor 26(4):2684–2728

    Article  MATH  Google Scholar 

  7. Cao Y, Long T, Sun J, Wang Z, Xu G (2023) Comparison of distributed task allocation algorithms considering non-ideal communication factors for multi-uav collaborative visit missions. IEEE Robot Autom Lett pp. 1–8

  8. Chakraa H, Guérin F, Leclercq E, Lefebvre D (2023) Optimization techniques for multi-robot task allocation problems: review on the state-of-the-art. Robot Auton Syst p. 104492

  9. Chen L, Liu WL, Zhong J (2022) An efficient multi-objective ant colony optimization for task allocation of heterogeneous unmanned aerial vehicles. J Comput Sci 58:101545

    Article  Google Scholar 

  10. Choudhury S, Gupta JK, Kochenderfer MJ, Sadigh D, Bohg J (2022) Dynamic multi-robot task allocation under uncertainty and temporal constraints. Auton Robot 46(1):231–247

    Article  MATH  Google Scholar 

  11. Cui W, Li R, Feng Y, Yang Y (2022) Distributed task allocation for a multi-uav system with time window constraints. Drones 6(9):226

    Article  MATH  Google Scholar 

  12. Dabass V, Sangwan S (2024) Swarm based optimization algorithms for task allocation in multi robot systems: a comprehensive review. Educ Adm: Theory Practice 30(4):2689–2695

    MATH  Google Scholar 

  13. Deng M, Yao Z, Li X, Wang H, Nallanathan A, Zhang Z (2023) Dynamic multi-objective awpso in dt-assisted uav cooperative task assignment. IEEE J Sel Areas Commun 41(11):3444–3460

    Article  MATH  Google Scholar 

  14. Fazal N, Khan MT, Anwar S, Iqbal J, Khan S (2022) Task allocation in multi-robot system using resource sharing with dynamic threshold approach. PLoS One 17(5):e0267982

    Article  MATH  Google Scholar 

  15. Fu J, Sun G, Liu J, Yao W, Wu L (2023) On hierarchical multi-uav dubins traveling salesman problem paths in a complex obstacle environment. IEEE Trans Cybern 54(1):123–135

    Article  MATH  Google Scholar 

  16. Gao X, Wang L, Yu X, Su X, Ding Y, Lu C, Peng H, Wang X (2023) Conditional probability based multi-objective cooperative task assignment for heterogeneous uavs. Eng Appl Artif Intell 123:106404

    Article  MATH  Google Scholar 

  17. Ghassemi P, Chowdhury S (2022) Multi-robot task allocation in disaster response: addressing dynamic tasks with deadlines and robots with range and payload constraints. Robot Auton Syst 147:103905

    Article  MATH  Google Scholar 

  18. Guo H, Miao Z, Ji J, Pan Q (2024) An effective collaboration evolutionary algorithm for multi-robot task allocation and scheduling in a smart farm. Knowledge-Based Syst 289:111474

    Article  Google Scholar 

  19. Hu C, Qu G, Zhang Y (2022) Pigeon-inspired fuzzy multi-objective task allocation of unmanned aerial vehicles for multi-target tracking. Appl Soft Comput 126:109310

    Article  MATH  Google Scholar 

  20. Kropp I, Nejadhashemi AP, Deb K (2023) Improved evolutionary operators for sparse large-scale multiobjective optimization problems. IEEE Trans Evol Comput 28(2):460–473

    Article  MATH  Google Scholar 

  21. Kumar S, Suganthan P (2021) A novel moea/d approach for multi-objective optimization problems with complex constraints. Soft Comput 25(3):1501–1515. https://doi.org/10.1007/s00542-021-06127-w

    Article  MATH  Google Scholar 

  22. Li D, Su H, Xu X, Wang Q, Qin J, Zou W (2023) Cooperative task scheduling and planning considering resource conflicts and precedence constraints. Int J Precis Eng Manuf 24(9):1503–1516

    Article  MATH  Google Scholar 

  23. Li L, Chen Z, Wang H, Kan Z (2023) Fast task allocation of heterogeneous robots with temporal logic and inter-task constraints. IEEE Robot Autom Lett 8(8):4991–4998

    Article  MATH  Google Scholar 

  24. Liu F, Dong X, Yu J, Hua Y, Li Q, Ren Z (2022) Distributed nash equilibrium seeking of \(n\)-coalition noncooperative games with application to uav swarms. IEEE Trans Netw Sci Eng 9(4):2392–2405

    Article  MathSciNet  MATH  Google Scholar 

  25. Mahato P, Saha S, Sarkar C, Shaghil M (2023) Consensus-based fast and energy-efficient multi-robot task allocation. Robot Auton Syst 159:104270

    Article  MATH  Google Scholar 

  26. Martin JG, Muros FJ, Maestre JM, Camacho EF (2023) Multi-robot task allocation clustering based on game theory. Robot Auton Syst 161:104314

    Article  MATH  Google Scholar 

  27. Ming F, Gong W, Wang L, Jin Y (2024) Constrained multi-objective optimization with deep reinforcement learning assisted operator selection. IEEE/CAA J Automatica Sinica 11(4):919–931

    Article  MATH  Google Scholar 

  28. Nguyen LX, Tun YK, Dang TN, Park YM, Han Z, Hong CS (2023) Dependency tasks offloading and communication resource allocation in collaborative uavs networks: a meta-heuristic approach. IEEE Internet Things J 10(10):9062–9076

    Article  Google Scholar 

  29. Peng K, Huang H, Zhao B, Jolfaei A, Xu X, Bilal M (2022) Intelligent computation offloading and resource allocation in iiot with end-edge-cloud computing using nsga-iii. IEEE Trans Netw Sci Eng 10(5):3032–3046

    Article  MathSciNet  Google Scholar 

  30. Peng Q, Wu H, Li N (2022) Modeling and solving the dynamic task allocation problem of heterogeneous uav swarm in unknown environment. Complexity 1:9219805

    Article  MATH  Google Scholar 

  31. Peng Q, Wu H, Li N, Wang F (2024) A dynamic task allocation method for unmanned aerial vehicle swarm based on wolf pack labor division model. IEEE Trans Emerg Topics Comput Intell 8(6):4075–4089

    Article  MATH  Google Scholar 

  32. Petrenko V, Tebueva F, Antonov V, Ryabtsev S, Pavlov A, Sakolchik A (2023) Method and algorithm for task allocation in a heterogeneous group of uavs in a clustered field of targets. J King Saud Univ-Comput Inf Sci 35(6):101580

    Google Scholar 

  33. Poudel S, Moh S (2022) Task assignment algorithms for unmanned aerial vehicle networks: a comprehensive survey. Vehicular Commun 35:100469

    Article  MATH  Google Scholar 

  34. Qi N, Huang Z, Zhou F, Shi Q, Wu Q, Xiao M (2022) A task-driven sequential overlapping coalition formation game for resource allocation in heterogeneous uav networks. IEEE Trans Mob Comput 22(8):4439–4455

    Article  MATH  Google Scholar 

  35. Qian T, Liu XF, Fang Y (2024) A cooperative ant colony system for multiobjective multirobot task allocation with precedence constraints. IEEE Trans Evolut Comput

  36. Quinton F, Grand C, Lesire C (2023) Market approaches to the multi-robot task allocation problem: a survey. J Intell & Robot Syst 107(2):29

    Article  MATH  Google Scholar 

  37. Skaltsis GM, Shin HS, Tsourdos A (2023) A review of task allocation methods for uavs. J Intell & Robot Syst 109(4):76

    Article  MATH  Google Scholar 

  38. Song F, Deng M, Xing H, Liu Y, Ye F, Xiao Z (2024) Energy-efficient trajectory optimization with wireless charging in uav-assisted mec based on multi-objective reinforcement learning. IEEE Trans Mob Comput 23(12):10867–10884

    Article  MATH  Google Scholar 

  39. Song F, Yang Q, Deng M, Xing H, Liu Y, Yu X, Li K, Xu L (2024) Aoi and energy tradeoff for aerial-ground collaborative mec: a multi-objective learning approach. IEEE Trans Mob Comput 23(12):11278–11294

    Article  Google Scholar 

  40. Song X, Cheng M, Lei L, Yang Y (2023) Multitask and multiobjective joint resource optimization for uav-assisted air-ground integrated networks under emergency scenarios. IEEE Internet Things J 10(23):20342–20357

    Article  MATH  Google Scholar 

  41. Song Z, Wang H, Xue B, Zhang M (2023) Balancing different optimization difficulty between objectives in multi-objective feature selection. IEEE Trans Evolut Comput

  42. de Sousa AL, de Oliveira AS (2024) Deadlock-free production using dempster–shafer and preset methods in predictive scheduling for multiagent controlled flexible manufacturing systems. Appl Soft Comput p. 111234

  43. Sui F, Tang X, Dong Z, Gan X, Luo P, Sun J (2023) Aco+ pso+ a*: a bi-layer hybrid algorithm for multi-task path planning of an auv. Comput & Ind Eng 175:108905

    Article  Google Scholar 

  44. Tan Y, Zhou C, Qian F (2024) Cooperative task allocation method for multi-unmanned aerial vehicles based on the modified genetic algorithm. IET Intel Transport Syst 18(6):1164–1173

    Article  MATH  Google Scholar 

  45. Tang J, Duan H, Lao S (2023) Swarm intelligence algorithms for multiple unmanned aerial vehicles collaboration: a comprehensive review. Artif Intell Rev 56(5):4295–4327

    Article  MATH  Google Scholar 

  46. Tian Y, Lu C, Zhang X, Cheng F, Jin Y (2020) A pattern mining-based evolutionary algorithm for large-scale sparse multiobjective optimization problems. IEEE Trans Cybern 52(7):6784–6797

    Article  MATH  Google Scholar 

  47. Wang J, Duan S, Ju S, Lu S, Jin Y (2022) Evolutionary task allocation and cooperative control of unmanned aerial vehicles in air combat applications. Robotics 11(6):124

    Article  MATH  Google Scholar 

  48. Wang L, Zhu D, Pang W, Zhang Y (2023) A survey of underwater search for multi-target using multi-auv: task allocation, path planning, and formation control. Ocean Eng 278:114393

    Article  Google Scholar 

  49. Wang S, Liu Y, Qiu Y, Li S, Zhou J (2024) An efficient distributed task allocation method for maximizing task allocations of multirobot systems. IEEE Trans Autom Sci Eng 21(3):3588–3602

    Article  MATH  Google Scholar 

  50. Wang T, Li Y, Yang D, Song W, Fu L, Ouyang M, Gao S (2023) Research on multi-uav target allocation based on improved auction algorithm. In CCF Conference on Computer Supported Cooperative Work and Social Computing, Springer, pp. 92–107

  51. Wang Y, Li H, Yao Y (2023) An adaptive distributed auction algorithm and its application to multi-auv task assignment. Sci China Technol Sci 66(5):1235–1244

    Article  MATH  Google Scholar 

  52. Wu H, Peng Q, Shi M, Xing L (2022) A survey of uav swarm task allocation based on the perspective of coalition formation. Int J Swarm Intell Res (IJSIR) 13(1):1–22

    Article  MATH  Google Scholar 

  53. Wu J, Zhang J, Li X, Gao L, Han G et al (2024) Multi-uav collaborative dynamic task allocation method based on isom and attention mechanism. IEEE Trans Veh Technol 73(5):6225–6235

    Article  MATH  Google Scholar 

  54. Xiong J, Li J, Li J, Kang S, Liu C, Yang C (2023) Probability-tuned market-based allocations for uav swarms under unreliable observations. IEEE Trans Cybern 53(11):6803–6814

    Article  MATH  Google Scholar 

  55. Xu J, Zhang Z, Hu Z, Du L, Cai X (2021) A many-objective optimized task allocation scheduling model in cloud computing. Appl Intell 51:3293–3310

    Article  MATH  Google Scholar 

  56. Xu Q, Su Z, Fang D, Wu Y (2024) Basic: distributed task assignment with auction incentive in uav-enabled crowdsensing system. IEEE Trans Veh Technol 73(2):2416–2430

    Article  MATH  Google Scholar 

  57. Xu S, Li L, Zhou Z, Mao Y, Huang J (2022) A task allocation strategy of the uav swarm based on multi-discrete wolf pack algorithm. Appl Sci 12(3):1331

    Article  MATH  Google Scholar 

  58. Xu W, Pi D, Wang H, Xie B (2022) Improved nsga-ii to solve a novel multi-objective task allocation problem with collaborative tasks. Proc Inst Mech Eng, Part D: J Automob Eng 236(14):3106–3123

    Article  MATH  Google Scholar 

  59. Yan F, Chu J, Hu J, Zhu X (2024) Cooperative task allocation with simultaneous arrival and resource constraint for multi-uav using a genetic algorithm. Expert Syst Appl 245:123023

    Article  Google Scholar 

  60. Yang M, Zhang A, Bi W, Wang Y (2022) A resource-constrained distributed task allocation method based on a two-stage coalition formation methodology for multi-uavs. J Supercomput 78(7):10025–10062

    Article  MATH  Google Scholar 

  61. Yu X, Gao X, Wang L, Wang X, Ding Y, Lu C, Zhang S (2022) Cooperative multi-uav task assignment in cross-regional joint operations considering ammunition inventory. Drones 6(3):77

    Article  MATH  Google Scholar 

  62. Zhang A, Zhang B, Bi W, Huang Z, Yang M (2023) Multi-uav task allocation based on gcn-inspired binary stochastic l-bfgs. Comput Commun 212:198–211

    Article  MATH  Google Scholar 

  63. Zhang J, Cui Y, Ren J (2023) Dynamic mission planning algorithm for uav formation in battlefield environment. IEEE Trans Aerosp Electron Syst 59(4):3750–3765

    Article  MATH  Google Scholar 

  64. Zhang Q, Li H (2007) Moea/d: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731

    Article  MATH  Google Scholar 

  65. Zhang R, Feng Y, Yang Y, Li X (2023) A deadlock-free hybrid estimation of distribution algorithm for cooperative multi-uav task assignment with temporally coupled constraints. IEEE Trans Aerosp Electron Syst 59(3):3329–3344

    Article  MATH  Google Scholar 

  66. Zhang Z, Jiang J, Haiyan X, Zhang WA (2024) Distributed dynamic task allocation for unmanned aerial vehicle swarm systems: a networked evolutionary game-theoretic approach. Chin J Aeronaut 37(6):182–204

    Article  MATH  Google Scholar 

  67. Zhao L, Hu Y, Wang B, Jiang X, Liu C, Zheng C (2023) A surrogate-assisted evolutionary algorithm based on multi-population clustering and prediction for solving computationally expensive dynamic optimization problems. Expert Syst Appl 223:119815

    Article  Google Scholar 

  68. Zheng D, Yf Zhang, Li F, Cheng P (2023) Uavs cooperative task assignment and trajectory optimization with safety and time constraints. Defence Technol 20:149–161

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Aircraft, Xihang University, Xi’an, 710072, Shaanxi, China

    Mi Yang, Zhifu Shi & Jiguang Li

  2. AVIC Xi’an Flight Automatic Control Research Institute, Xi’an, 710076, Shaanxi, China

    Baichuan Zhang

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  1. Mi Yang
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Contributions

In the paper "A Hybrid Task Allocation Approach for Multi-UAV Systems with Complex Constraints: A Market-Based Bidding Strategy and Improved NSGA-III Optimization" the contributions of the authors Yang Mi, Zhang Baichuan, Shi Zhifu, and Li Jiguang are as follows: Yang Mi focused on the design of the research framework and theoretical analysis, developing the fundamental theory and models for collaborative task allocation under complex time and resource constraints. Zhang Baichuan was primarily responsible for the design and implementation of the optimization algorithms for task allocation, conducting simulations and testing to validate the effectiveness and performance of these algorithms. Shi Zhifu concentrated on building the system architecture and experimental environment to ensure practical application of the proposed method in real multi-UAV systems, along with data collection and analysis. Lastly, Li Jiguang took charge of the overall writing and organization of the paper, ensuring the clarity and coherence of the research findings while conducting a comparative analysis with relevant literature to enhance the depth and breadth of the study. Through this collaborative effort, the authors advanced the field of multi-UAV collaborative task allocation.

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Correspondence to Mi Yang.

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Yang, M., Zhang, B., Shi, Z. et al. A hybrid task allocation approach for multi-UAV systems with complex constraints: a market-based bidding strategy and improved NSGA-III optimization. J Supercomput 81, 546 (2025). https://doi.org/10.1007/s11227-025-07027-x

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