Skip to main content

Distributed spot welding task allocation and sequential planning for multi-station multi-robot coordinate assembly processes


Many industrial robots are equipped in the multi-station autobody assembly line to complete the spot welding tasks collaboratively. The task allocation and the sequential planning of the welding spots (WSs) are two key sub-problems and significantly influence the efficiency of the multi-station multi-robot (MSMR) coordination process. However, these two sub-problems are highly coupled and have complex engineering constraints, which makes them hard to be jointly optimized. Traditional methods often optimize the MSMR coordination process through hierarchical optimization, which does not fully consider the coupling effects among constraints and is easy to be trapped in the local optima. In this work, an integrated MSMR task allocation and sequential planning framework is proposed to fully consider the complex engineering constraints (e.g., robot accessibility, collisions, and the cycle time at the station) to model the coordination process. An enhanced biased random key genetic algorithm (BRKGA) is proposed to optimize the proposed framework by explicitly considering the engineering constraints and tackling the local optimality caused by the coupling effects between two sub-problems, in which double-crossover(), double-mutation(), and elite re-optimization() are designed to simultaneously ensure the adjacent robots are assigned distinct sets of welding spots and reduce the time of each robot in completing the welding task. In order to evaluate the effectiveness of the proposed method, the spot welding of the autobody is used as the case study. Compared with the two benchmark methods, the line balance efficiency is improved by 21.087% and 7.803%, respectively.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17


  1. Li Z, Xu Y, Fang S, Wang Y, Zheng X (2020) Multi-objective coordinated energy dispatch and voyage scheduling for a multienergy ship microgrid. IEEE Trans Ind Appl 56(2):989–999

    Article  Google Scholar 

  2. Pellegrinelli S, Pedrocchi N, Molinari-Tosatti L, Fischer A, Tolio T (2014) Multi-robot spot-welding cell design: problem formalization and proposed architecture. Procedia CIRP 21:324–329

    Article  Google Scholar 

  3. Pellegrinelli S, Pedrocchi N, Tosatti LM, Fischer A, Tolio T (2017) Multi-robot spot-welding cells for car-body assembly: design and motion planning. Robot Comput Integr Manuf 44:97–116

    Article  Google Scholar 

  4. Lippi M, Marino A (2021) A mixed-integer linear programming formulation for human multi-robot task allocation. In: 2021 30th IEEE International Conference on Robot Human Interactive Communication (RO-MAN). pp 1017–1023

  5. Lotfi M, Osório GJ, Javadi MS, Ashraf A, Zahran M, Samih G, Catalão JPS (2021) A dijkstra-inspired graph algorithm for fully autonomous tasking in industrial applications. IEEE Trans Ind Appl 57(5):5448–5460

    Article  Google Scholar 

  6. Bao B, Yang Y, Chen Q, Liu A, Zhao J (2016) Task allocation optimization in collaborative customized product development based on double-population adaptive genetic algorithm. J Intell Manuf 27(5):1097–1110

    Article  Google Scholar 

  7. Wong C, Shackleford S, Potter D, Richardson J-P, McDermott L, Nolan J (2022) Robotic task sequencing and motion coordination for multiarm systems. IEEE/ASME Trans Mechatron 27(6):5275–5286

    Article  Google Scholar 

  8. Jones EG, Dias MB, Stentz A (2011) Time-extended multi-robot coordination for domains with intra-path constraints. Auton Robot 30(1):41–56

    Article  Google Scholar 

  9. Garapati K, Roldán JJ, Garzón M, Del Cerro J, Barrientos A (2017) A game of drones: game theoretic approaches for multi-robot task allocation in security missions. In: Iberian Robotics Conference. pp 855–866

  10. Zitouni F, Maamri R, Harous S (2019) FA-QABC-MRTA: a solution for solving the multi-robot task allocation problem. Intel Serv Robot 12(4):407–418

    Article  Google Scholar 

  11. Elango M, Nachiappan S, Tiwari MK (2011) Balancing task allocation in multi-robot systems using k-means clustering and auction based mechanisms. Expert Syst Appl 38(6):6486–6491

    Article  Google Scholar 

  12. Kartal B, Nunes E, Godoy J, Gini M (2016) Monte Carlo tree search for multi-robot task allocation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 30

  13. Zacharia PT, Xidias EK, Aspragathos NA (2013) Task scheduling and motion planning for an industrial manipulator. Robot Comput Integr Manuf 29(6):449–462.

    Article  Google Scholar 

  14. Kovacs A (2013) Task sequencing for remote laser welding in the automotive industry. In: Proceedings of the Twenty-Third International Conference on International Conference on Automated Planning and Scheduling. pp 457–461

  15. Gentilini I, Margot F, Shimada K (2013) The travelling salesman problem with neighbourhoods: MINLP solution. Optim Methods Softw 28(2):364–378.

    Article  MathSciNet  MATH  Google Scholar 

  16. Alatartsev S, Stellmacher S, Stellmacher S (2015) Robotic task sequencing problem: a survey. Robotic Task Sequencing Problem: A Survey 80

  17. Suarez-Ruiz F, Lembono TS, Pham Q-C (2018) ROBOTSP - a fast solution to the robotic task sequencing problem. In: 2018 IEEE International Conference on Robotics and Automation (ICRA). pp 1611–1616

  18. Touzani H, Hadj-Abdelkader H, Seguy N, Bouchafa S (2021) Multi-robot task sequencing and automatic path planning for cycle time optimization: application for car production line. IEEE Robot Autom Lett 6(2):1335–1342.

    Article  Google Scholar 

  19. Chen C-H, Chou F-I, Chou J-H (2022) Optimization of robotic task sequencing problems by crowding evolutionary algorithms. IEEE Trans Syst Man Cybern Syst 52(11):6870–6885.

    Article  Google Scholar 

  20. Guangbao Z, Jianhua W (2020) Multi-station and multi-robot welding path planning based on greedy interception algorithm. In: 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). IEEE, pp 1190–1195

  21. Spensieri D, Carlson JS, Ekstedt F, Bohlin R (2016) An iterative approach for collision free routing and scheduling in multirobot stations. IEEE Trans Autom Sci Eng 13(2):950–962

    Article  Google Scholar 

  22. Lopes TC, Sikora CGS, Molina RG, Schibelbain D, Rodrigues LC, Magatão L (2017) Balancing a robotic spot welding manufacturing line: an industrial case study. Eur J Oper Res 263(3):1033–1048

  23. Glorieux E, Riazi S, Lennartson B (2018) Productivity/energy optimisation of trajectories and coordination for cyclic multi-robot systems. Robot Comput Integr Manuf 49:152–161

    Article  Google Scholar 

  24. Spensieri D, Bohlin R, Carlson JS (2013) Coordination of robot paths for cycle time minimization. In: 2013 IEEE International Conference on Automation Science and Engineering (CASE). IEEE, pp 522–527

  25. Gombolay MC, Wilcox RJ, Shah JA (2018) Fast scheduling of robot teams performing tasks with temporospatial constraints. IEEE Trans Rob 34(1):220–239

    Article  Google Scholar 

  26. Zhou B, Zhou R, Gan Y, Fang F, Mao Y (2022) Multi-robot multi-station cooperative spot welding task allocation based on stepwise optimization: an industrial case study. Robot Comput Integr Manuf 73:102197

    Article  Google Scholar 

  27. Martin JG, Frejo JRD, García RA, Camacho EF (2021) Multi-robot task allocation problem with multiple nonlinear criteria using branch and bound and genetic algorithms. Intell Serv Robot 14(5):707–727

    Article  Google Scholar 

  28. Li Z, Janardhanan MN, Ponnambalam SG (2020) Cost-oriented robotic assembly line balancing problem with setup times: multi-objective algorithms. J Intell Manuf 1–19

  29. Nilakantan JM, Nielsen IE, Ponnambalam SG, Venkataramanaiah S (2017) Differential evolution algorithm for solving RALB problem using cost- and time-based models. Int J Adv Manuf Technol 89:311–332

    Article  Google Scholar 

  30. Zhang Z, Ma S, Jiang X (2022) Research on multi-objective multi-robot task allocation by Lin-Kernighan-Helsgaun guided evolutionary algorithms. Mathematics 10(24)

  31. Zhang S, Pecora F (2021) Online sequential task assignment with execution uncertainties for multiple robot manipulators. IEEE Robot Autom Lett 6(4):6993–7000

    Article  Google Scholar 

  32. Liu Y, Zhao W, Lutz T, Yue X (2021) Task allocation and coordinated motion planning for autonomous multi-robot optical inspection systems. J Intell Manuf 1–14

  33. Gonçalves JF, Resende MG (2011) Biased random-key genetic algorithms for combinatorial optimization. J Heuristics 17(5):487–525

    Article  Google Scholar 

  34. Jing W, Deng D, Wu Y, Shimada K (2020) Multi-UAV coverage path planning for the inspection of large and complex structures. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp 1480–1486

  35. Noronha TF, Resende MG, Ribeiro CC (2011) A biased random-key genetic algorithm for routing and wavelength assignment. J Global Optim 50(3):503–518

    Article  Google Scholar 

  36. Rezaei N, Uddin MN, Amin IK, Othman ML, Marsadek M (2019) Genetic algorithm-based optimization of overcurrent relay coordination for improved protection of DFIG operated wind farms. IEEE Trans Ind Appl 55(6):5727–5736

    Article  Google Scholar 

  37. Wang Z, Geng X, Shao Z (2009) An effective simulated annealing algorithm for solving the traveling salesman problem. J Comput Theor Nanosci 6:1680–1686

    Article  Google Scholar 

  38. Kuffner JJ, LaValle SM (2000) RRT-connect: an efficient approach to single-query path planning. In: IEEE International Conference on Robotics and Automation (ICRA). vol 2. IEEE, pp 995–1001

  39. Liu Y, Zhao W, Sun R, Yue X (2020) Optimal path planning for automated dimensional inspection of free-form surfaces. J Manuf Syst 56:84–92

Download references


Dr. Liu was partially funded by National Natural Science Foundation of China (51875362), Natural Science Foundation of Shanghai (21ZR1444500), and Shanghai Pujiang Program (22PJD048).

Author information

Authors and Affiliations



All authors contributed to the study conception and design. Conceptualization of this study was performed by Wenzheng Zhao, Yinhua Liu, Yinan Wang, and Xiaowei Yue. Methodology, software, and first draft were completed by Wenzheng Zhao and Yinhua Liu. Draft revision and finalization were performed by Yinan Wang and Xiaowei Yue. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Yinhua Liu or Yinan Wang.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, W., Liu, Y., Wang, Y. et al. Distributed spot welding task allocation and sequential planning for multi-station multi-robot coordinate assembly processes. Int J Adv Manuf Technol 127, 5233–5251 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: