Hostname: page-component-857557d7f7-ksgrx Total loading time: 0 Render date: 2025-12-11T07:34:36.216Z Has data issue: false hasContentIssue false

Workspace orientated optimization design of serial-parallel robotic collaborative system

Published online by Cambridge University Press:  10 December 2025

Fanwei Ye
Affiliation:
Laboratory of Electromechanical Coupling in Electronic Equipment, Xidian University, Xi’an, China
Xuechao Duan*
Affiliation:
Laboratory of Electromechanical Coupling in Electronic Equipment, Xidian University, Xi’an, China
Tao Zha
Affiliation:
Laboratory of Electromechanical Coupling in Electronic Equipment, Xidian University, Xi’an, China
Jun Liu
Affiliation:
Laboratory of Electromechanical Coupling in Electronic Equipment, Xidian University, Xi’an, China
Xiangfei Meng
Affiliation:
Laboratory of Electromechanical Coupling in Electronic Equipment, Xidian University, Xi’an, China
Guodong Tan
Affiliation:
Laboratory of Electromechanical Coupling in Electronic Equipment, Xidian University, Xi’an, China
*
Corresponding author: Xuechao Duan; Email: xchduan@xidian.edu.cn

Abstract

In aerospace, automated assembly line, and precision engineering, asymmetric multi-robot systems comprising serial and parallel robots leverage the complementary strengths of these configurations to address the conflicting demands of high load capacity, extensive range, and flexibility in assembly tasks. However, the relatively small workspace of the parallel robot limits the full potential of the collaborative system functionality. This paper centers on a collaborative assembly system involving serial-parallel robots, whose collaborative workspace is determined by using a combination of the Monte Carlo method and lattice method. Additionally, a multi-objective optimization model is developed to holistically evaluate the collaborative workspace performance. The optimization problem is solved by an enhanced NSGA-II algorithm, which yields a Pareto optimal solution set. This result offers valuable technical insights for designing collaborative systems tailored to diverse task requirements.

Information

Type
Research Article
Copyright
© The Author(s), 2025. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Burgard, W., Moors, M., Stachniss, C. and Schneider, F. E., “Coordinated multi-robot exploration,” IEEE Trans. Robot. 21, 376386 (2005).10.1109/TRO.2004.839232CrossRefGoogle Scholar
Marvel, J. A., Bostelman, R. and Falco, J., “Multi-robot assembly strategies and metrics,” ACM Comput. Surv. 51, 132 (2018).10.1145/3150225CrossRefGoogle ScholarPubMed
Cui, G., Zhou, H., Zhang, Y. and Zhou, H., “Multi-Objective Optimization of a Parallel Fine-Tuning Manipulator for Segment Assembly Robots in Shield Tunneling Machines,Mechatronics and Robotics Engineering for Advanced and Intelligent Manufacturing. Cham: Springer International Publishing, 217239 (2016).Google Scholar
Tan, G. D., Meng, X. F., Duan, X. C., Cheng, L. L., Niu, D. C., He, S. and Zhang, D., “Kinematic-map-model-guided analysis and optimization of 2-PSS&1-RR circular-rail parallel mechanism for fully steerable phased array antennas,” Defence Technol. 38, 136154 (2024).10.1016/j.dt.2024.03.001CrossRefGoogle Scholar
He, S., Duan, X., Qu, X. and Xiao, X. J., “Kinematic modeling and motion control of a parallel robotic antenna pedestal,” Robotica 41, 32753295 (2023).10.1017/S0263574723000917CrossRefGoogle Scholar
Erastova, K., “Effective workspaces of parallel robots,” Robotica 40, 43084325 (2022).10.1017/S0263574722000911CrossRefGoogle Scholar
Flores-Salazar, E. D., Lugo-González, E., Arias-Montiel, M. and Gallardo-Alvarado, J., “A robust control scheme for a 2PUS+RR parallel robot for ankle rehabilitation,” Robotica 41, 32963313 (2023).10.1017/S0263574723000978CrossRefGoogle Scholar
Lei, J. and Song, G., “Six-dimensional constraints and force feedback for robot-assisted teleoperated fracture reduction,” Robotica 42, 23282344 (2024).10.1017/S0263574724000687CrossRefGoogle Scholar
Liu, Q., Gong, Z., Nie, Z. and Liu, X. J., “Enhancing the terrain adaptability of a multirobot cooperative transportation system via novel connectors and optimized cooperative strategies,” Front. Mech. Eng. 18, 38 (2023).10.1007/s11465-023-0754-2CrossRefGoogle Scholar
Chakraa, H., Guérin, F., Leclercq, E. and Lefebvre, D., “Optimization techniques for Multi-Robot Task Allocation problems: Review on the state-of-the-art,” Robot. Auton. Syst. 168, 104492 (2023).10.1016/j.robot.2023.104492CrossRefGoogle Scholar
Zhou, Y., Xiao, J. H., Zhou, Y. and Loianno, G., “Multi-robot collaborative perception with graph neural networks,” IEEE Robot. Autom. Lett. 7, 22892296 (2022).10.1109/LRA.2022.3141661CrossRefGoogle Scholar
Ban, C., Fu, B., Wei, W., Chen, Z., Guo, S., Deng, N., Yuan, L. and Long, Y., “A multi-objective trajectory planning approach for vibration suppression of a series–parallel hybrid flexible welding manipulator,” Mech. Syst. Signal Proc. 220, 111678 (2024).10.1016/j.ymssp.2024.111678CrossRefGoogle Scholar
Abdeldaim, A. M., Abdelrahman, A. E., Ismail, M. M. H., Youssef, K. E. H. and Alkhedher, M.. “Automated Motion System of 5-DOF Hybrid Serial-Parallel Manipulator for Invasive Medical Procedures.” In: 2025 13th International Conference on Intelligent Control and Information Processing (ICICIP) (IEEE, 2025).10.1109/ICICIP64458.2025.10898140CrossRefGoogle Scholar
Feng, Z., Hu, G., Sun, Y. and Soon, J., “An overview of collaborative robotic manipulation in multi-robot systems,” Annu. Rev. Control 49, 113127 (2020).10.1016/j.arcontrol.2020.02.002CrossRefGoogle Scholar
Wang, D., “Theoretical foundation of metamorphic mechanism and its synthesis,” Chin. J. Mech. Eng. 43(08), 32 (2007).10.3901/JME.2007.08.032CrossRefGoogle Scholar
Parker, L. E.. “Decision Making as Optimization in Multi-robot Teams.” In: Proc. International Conference on Distributed Computing and Internet Technology (2012) pp. 3549.Google Scholar
Wang, R., Song, Y. and Dai, J. S., “Reconfigurability of the origami-inspired integrated 8R kinematotropic metamorphic mechanism and its evolved 6R and 4R mechanisms,” Mech. Mach. Theory 161, 104245 (2021).10.1016/j.mechmachtheory.2021.104245CrossRefGoogle Scholar
Chen, G. Y., Cheng, Q. L., Zhang, J. Y., Hong, H. B. and He, J., “Design of an automated docking system for spacecraft cabin sections based on online attitude adjustment,Missile Space Launch Technol. 2020, 99106 (2020).Google Scholar
Guo, H. J., “Automatic docking and assembly technology for large aircraft components,” Aerosp. Manuf. Technol. 2013, 7275 (2013).Google Scholar
Schmitt, R., Corves, B., Loosen, P., Brecher, C., Jeschke, S., Kimmelmann, W., Hüsing, M., Stollenwerk, J., Bertelsmeier, F., Detert, T., Haag, S., Hoffmann, M., Holters, M., Kurtenbach, S., Permin, E., Prochnau, M., Stor, C.K., Janßen, M. Cognition-Enhanced, Self-Optimizing Assembly Systems (Springer, Berlin, 2017).10.1007/978-3-319-47452-6_10CrossRefGoogle Scholar
Ramirez, J. and Wollnack, J., “Flexible automated assembly systems for large CFRP-structures,” Proc. Technol. 15, 447455 (2014).10.1016/j.protcy.2014.09.004CrossRefGoogle Scholar
Reld, E., “Development of a Mobile Driling and Fastening System based on a PKM Robotic Platform,” In: SAE Aerotech Congress & Exhibition, Seattle, United States (2015), pp. 20592070.Google Scholar
Jia, G., Huang, H., Wang, S. and Li, B., “Type synthesis of plane-symmetric deployable grasping parallel mechanisms using constraint force parallelogram law,” Mech. Mach. Theory 161, 104330 (2021).10.1016/j.mechmachtheory.2021.104330CrossRefGoogle Scholar
Romdhane, L., “Design and analysis of a hybrid serial-parallel manipulator,” Mech. Mach. Theory 34, 10371055 (1999).10.1016/S0094-114X(98)00079-2CrossRefGoogle Scholar
Xu, Z. B., Zhao, Z. Y., He, S., He, J. P. and Wu, Q. W., “Improvement of the Monte Carlo method for solving robot workspace and volume calculation,” Optical Prec. Eng. 26, 27032713 (2018).Google Scholar
Peidró, A., Reinoso, O., Gil, A., Marín, J. M. and Payá, L., “An improved Monte Carlo method based on Gaussian growth to calculate the workspace of robots,” Eng. Appl. Artif. Intel. 64, 197207 (2017).10.1016/j.engappai.2017.06.009CrossRefGoogle Scholar
Rega, A., Marino, C. D., Pasquariello, A., Vitolo, F., Patalano, S., Zanella, A. and Lanzotti, A., “Collaborative workplace design: A knowledge-based approach to promote human-robot integration and multi-objective layout optimization,Appl. Sci. 11(24), 12147 (2021).10.3390/app112412147CrossRefGoogle Scholar
Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T., “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evolut. Comput. 6, 182197 (2002).10.1109/4235.996017CrossRefGoogle Scholar
Chen, H. P. and Cheng, H. T., “Online performance optimization for complex robotic assembly processes,” J. Manuf. Process. 72, 544552 (2021).10.1016/j.jmapro.2021.10.047CrossRefGoogle Scholar
Boanta, C. and Brişan, C., ``Estimation of the kinematics and workspace of a robot using artificial neural networks,” Sensors 22, 8356 (2022).10.3390/s22218356CrossRefGoogle ScholarPubMed
Zhu, L., Chen, J. Y., Zhang, H. and Zhang, W., “Multi-robot environmental coverage with a two-stage coordination strategy via deep reinforcement learning,” IEEE Trans. Intell. Transp. Syst. 25, 50225033 (2024).10.1109/TITS.2023.3333409CrossRefGoogle Scholar