Hostname: page-component-745bb68f8f-cphqk Total loading time: 0 Render date: 2025-02-10T01:53:42.606Z Has data issue: false hasContentIssue false
  • English
  • Français

Dimensional multi-objective optimization design for 2RPU-RPS parallel mechanism

Published online by Cambridge University Press:  07 February 2025

Siyang Peng Open the ORCID record for Siyang Peng [Opens in a new window] ,
Li Du ,
Linxian Che  and
Zujin Jin Open the ORCID record for Zujin Jin [Opens in a new window]
Siyang Peng
Affiliation:
School of Mechanical Engineering, Chongqing Technology and Business University, Chongqing, China
Li Du*
Affiliation:
School of Mechanical Engineering, Chongqing Technology and Business University, Chongqing, China
Linxian Che*
Affiliation:
School of Intelligence Manufacturing and Traffic, Chongqing Vocational Institute of Engineering, Chongqing, China
Zujin Jin
Affiliation:
School of Mechanical and Electronic Engineering, Suzhou University, Suzhou, China
*
Corresponding authors: Li Du; Email: [email protected] and Linxian Che; Email: [email protected]
Corresponding authors: Li Du; Email: [email protected] and Linxian Che; Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

A modeling method of multi-objective optimization design for parallel mechanisms (PMs) is proposed, whose implementation is illustrated with 2RPU-RPS mechanism as an example. The orientation of biased output axis on moving platform is depicted by spherical attitude angles, and its kinematic model is deduced through vector method. With screw theory as mathematic tool, a comprehensive evaluation method of kinematic performance for PM is established. On this basis, the expensive constrained multi-objective optimization model of dimensional parameters for the discussed mechanism is constructed. The NSDE-II algorithm, formed by replacing the genetic algorithm operators in non-dominated sorting genetic algorithm II (NSGA-II) with DE operators, is utilized to solve this multi-objective optimization problem, thus obtaining multiple Pareto optimal solutions with engineering application significance, which proves the feasibility and effectiveness of the proposed modeling method and algorithm. Moreover, the normalization coverage space and the minimum adjacent vector angle are proposed to evaluate the computational performance of NSDE-II. Finally, the potential engineering application value for the optimized 2RPU-RPS PM is presented.

Keywords

parallel mechanismmotion/force transmission performanceregular transmission orientation workspacemulti-objective optimizationdifferential evolution algorithm
Type
Research Article
Information
Robotica , First View , pp. 1 - 23
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.)

References

Dong, C., Liu, H. and Yang, J., “Dimensional synthesis of a novel asymmetric 5-DOF hybrid robot,” Chin. Mech. Eng. 32(10), 2418–2426 (2021).Google Scholar
Yang, C., Ye, W. and Li, Q., “Review of the performance optimization of parallel manipulators,” Mech. Mach. Theory 170, p,–104725 (2022).CrossRefGoogle Scholar
Song, J., Zhao, C., Zhao, K., Yan, W. and Chen, Z., “Singularity analysis and dimensional synthesis of 2R1T 3-UPU parallel mechanism based on performance atlas,” ASME J. Mech. Robot. 15(1), 011001 (2023).CrossRefGoogle Scholar
Xu, Y., Tong, S., Wang, B., Ju, Z., Yao, J. and Zhao, Y., “Application of 2RPU-UPR parallel mechanism in antenna support,” Chin. Mech. Eng. 30(14), 1748–1755 (2019).Google Scholar
Yang, Y., Zhou, Z., Deng, Y., Duan, Y., Dou, Y., Zeng, D. and Hou, Y., “Dynamics optimization and simulation of 3-RSR parallel vehicle-borne antenna mechanisms,” Chin. Mech. Eng. 30(10), 1219–1225 (2019).Google Scholar
Wang, L., Xu, H. and Guan, L., “Optimal design of 3-PUU parallel mechanism with 2R1T DOFs,” Mech. Mach. Theory 144, 190–203 (2017).CrossRefGoogle Scholar
Matteo, R., Saioa, H., Oscar, A. and Ceccarelli, M., “Kinematic analysis and multi-objective optimization of a 3-UPR parallel mechanism for a robotic leg,” Mech. Mach. Theory 120, pp,190–202 (2018).Google Scholar
Gao, Z. and Zhang, D., “Performance analysis, mapping, and multi-objective optimization of a hybrid robotic machine tool,” IEEE Trans. Ind. Electron. 62(1), 423–433 (2015).CrossRefGoogle Scholar
Zhang, W., Li, J., Ye, M. and Yang, C., “Multi-objective optimization of design synthesis for 2-PUR-PSR parallel manipulator,” Trans. Chin. Soc. Ag. Mach. 51(11), 403–410 (2020).Google Scholar
Li, X., Qu, H. and Li, G., “Optimal design of a kinematically redundant planar parallel mechanism based on error sensitivity and workspace,” Trans. ASME J. Mech. Des. 145(2), 023305 (2022).CrossRefGoogle Scholar
Peng, S., Cheng, Z., Che, L., Zheng, Y. and Cao, S., “Configuration design and dimensional synthesis of an asymmetry 2R1T parallel mechanism,” Robotica 41(2), 713–734 (2023).CrossRefGoogle Scholar
Zhao, J., Li, R., Li, X., Pang, G., Zhang, J. and Kang, S., “Workspace and sorting application of (2-RPU/RPS)&R hybrid mechanism,” Pkg. Eng. 42(5), 209–215 (2021).Google Scholar
Chen, X., Xie, F. and Liu, X., “Evaluation of the maximum value of motion/force transmission power in parallel manipulators,” J. Mech. Eng. 50(3), 1–9 (2014).Google Scholar
Che, L., Wang, T. and He, B., “Dimensional synthesis of a 2PUR-PSR parallel manipulator with optimal orientational capability,” J. Mach. Des. 36(12), 108–115 (2019).Google Scholar
Xie, F., Liu, X. and Li, T., “A comparison study on the orientation capability and parasitic motions of two novel articulated tool heads with parallel kinematics,” Adv. Mech. Eng. 5, 249103 (2015).CrossRefGoogle Scholar
Zhang, W., Xu, L., Tong, J. and Li, Q., “Kinematic analysis and dimensional synthesis of 2-PUR-PSR parallel manipulator,” J. Mech. Eng. 54(7), pp,45–52 (2018).CrossRefGoogle Scholar
Deb, K., Pratap, A., Agarwal, S. and Meyarivan, T., “A fast and elisit multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Comput. 6(2), 182–197 (2002).CrossRefGoogle Scholar
Opara, K. and Arabas, J., “Differential evolution: A survey of theoretical analyses,” Swarm Evol. Comput. 44, 546–558 (2019).CrossRefGoogle Scholar
Wang, L., Ren, Y., Qiu, Q., “Survey on performance indicators for multi-objective evolutionary algorithms [J],” Chin. J. Comput. 44(8), 1590–1619 (2021).Google Scholar
Che, L., Chen, G., Jiang, H., Du, L. and Wen, S., “Dimensional synthesis for a Rec4 parallel mechanism with maximum transmission workspace,” Mech Mach. Theory 153(3), p,–14008 (2020).CrossRefGoogle Scholar
Zhang, Q. and Li, H., “MOEA/D: A multi-objective evolutionary algorithm based on decomposition,” IEEE Trans. Evol. Comput. 11(6), 712–731 (2007).CrossRefGoogle Scholar
Wang, X., “Status and development of detection technique and equipment of hydraulic support,” Coal Mine Technol. 21(6), 1–5 (2016).Google Scholar