No CrossRef data available.
Article contents
Workspace orientated optimization design of serial-parallel robotic collaborative system
Published online by Cambridge University Press: 10 December 2025
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.
Keywords
Information
- Type
- Research Article
- Information
- Copyright
- © The Author(s), 2025. Published by Cambridge University Press
References
Burgard, W., Moors, M., Stachniss, C. and Schneider, F. E., “Coordinated multi-robot exploration,” IEEE Trans. Robot. 21, 376–386 (2005).CrossRefGoogle Scholar
Marvel, J. A., Bostelman, R. and Falco, J., “Multi-robot assembly strategies and metrics,” ACM Comput. Surv. 51, 1–32 (2018).CrossRefGoogle 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, 217–239 (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, 136–154 (2024).CrossRefGoogle Scholar
He, S., Duan, X., Qu, X. and Xiao, X. J., “Kinematic modeling and motion control of a parallel robotic antenna pedestal,” Robotica 41, 3275–3295 (2023).CrossRefGoogle Scholar
Erastova, K., “Effective workspaces of parallel robots,” Robotica 40, 4308–4325 (2022).CrossRefGoogle 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, 3296–3313 (2023).CrossRefGoogle Scholar
Lei, J. and Song, G., “Six-dimensional constraints and force feedback for robot-assisted teleoperated fracture reduction,” Robotica 42, 2328–2344 (2024).CrossRefGoogle 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).CrossRefGoogle 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).CrossRefGoogle Scholar
Zhou, Y., Xiao, J. H., Zhou, Y. and Loianno, G., “Multi-robot collaborative perception with graph neural networks,” IEEE Robot. Autom. Lett. 7, 2289–2296 (2022).CrossRefGoogle 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).CrossRefGoogle 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).CrossRefGoogle Scholar
Feng, Z., Hu, G., Sun, Y. and Soon, J., “An overview of collaborative robotic manipulation in multi-robot systems,” Annu. Rev. Control 49, 113–127 (2020).CrossRefGoogle Scholar
Wang, D., “Theoretical foundation of metamorphic mechanism and its synthesis,” Chin. J. Mech. Eng. 43(08), 32 (2007).CrossRefGoogle Scholar
Parker, L. E.. “Decision Making as Optimization in Multi-robot Teams.” In: Proc. International Conference on Distributed Computing and Internet Technology (2012) pp. 35–49.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).CrossRefGoogle 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, 99–106 (2020).Google Scholar
Guo, H. J., “Automatic docking and assembly technology for large aircraft components,” Aerosp. Manuf. Technol. 2013, 72–75 (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).CrossRefGoogle Scholar
Ramirez, J. and Wollnack, J., “Flexible automated assembly systems for large CFRP-structures,” Proc. Technol. 15, 447–455 (2014).CrossRefGoogle 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. 2059–2070.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).CrossRefGoogle Scholar
Romdhane, L., “Design and analysis of a hybrid serial-parallel manipulator,” Mech. Mach. Theory 34, 1037–1055 (1999).CrossRefGoogle 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, 2703–2713 (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, 197–207 (2017).CrossRefGoogle 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).CrossRefGoogle Scholar
Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T., “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evolut. Comput. 6, 182–197 (2002).CrossRefGoogle Scholar
Chen, H. P. and Cheng, H. T., “Online performance optimization for complex robotic assembly processes,” J. Manuf. Process. 72, 544–552 (2021).CrossRefGoogle Scholar
Boanta, C. and Brişan, C., ``Estimation of the kinematics and workspace of a robot using artificial neural networks,” Sensors 22, 8356 (2022).CrossRefGoogle 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, 5022–5033 (2024).CrossRefGoogle Scholar