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Improving machining trajectory accuracy for dual-robot system with in situ laser measurement and meta-heuristic optimization

Published online by Cambridge University Press:  02 April 2025

Zhentao Zhang Open the ORCID record for Zhentao Zhang [Opens in a new window] ,
Guangde Bi ,
Jiajing Li ,
Lining Sun  and
Lei Lu Open the ORCID record for Lei Lu [Opens in a new window]
Zhentao Zhang
Affiliation:
Jiangsu Provincial Key Laboratory of Advanced Robotics & Collaborative Innovation Center of Suzhou Nano Science and Technology, Soochow University, Suzhou, China
Guangde Bi
Affiliation:
Jiangsu Provincial Key Laboratory of Advanced Robotics & Collaborative Innovation Center of Suzhou Nano Science and Technology, Soochow University, Suzhou, China
Jiajing Li
Affiliation:
Jiangsu Provincial Key Laboratory of Advanced Robotics & Collaborative Innovation Center of Suzhou Nano Science and Technology, Soochow University, Suzhou, China
Lining Sun
Affiliation:
Jiangsu Provincial Key Laboratory of Advanced Robotics & Collaborative Innovation Center of Suzhou Nano Science and Technology, Soochow University, Suzhou, China State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China
Lei Lu*
Affiliation:
Jiangsu Provincial Key Laboratory of Advanced Robotics & Collaborative Innovation Center of Suzhou Nano Science and Technology, Soochow University, Suzhou, China
*
Corresponding author: Lei Lu; Email: [email protected]
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Abstract

Industrial robots are widely utilized in the machining of complex parts because of their flexibility. However, their low positioning accuracy and spatial geometric error characteristics significantly limit the contour precision of robot machined parts. Therefore, in the robot machining procedure, an in situ measurement system is typically required. This study aims to enhance the trajectory accuracy of robotic machining through robotic in situ measurement and meta-heuristic optimization. In this study, a measurement-machining dual-robot system for measurement and machining is established, consisting of a measurement robot with a laser sensor mounted at the robot end and a machining robot equipped with a machining tool. In the measuring process, high-precision standard spheres are set on the edge of the machining area, and the high-precision standard geometry is measured by the measurement robot. According to measured geometry information in the local area, the trajectory accuracy for the machining robot is improved. By utilizing the standard radius of the standard spheres and adopting a meta-heuristic optimization algorithm, this study addresses the complexity of the robot kinematics model, while also overcoming local optima commonly introduced by gradient-based iterative methods. The results of the experiments in this study confirm that the proposed method markedly refines the precision of the robot machining trajectory.

Keywords

measurement-machining robot systemin situ laser measurementrobot calibrationrobot machining trajectory improvementmeta-heuristic optimization method
Type
Research Article
Information
Robotica , First View , pp. 1 - 21
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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References

Rosyid, A., Stefanini, C. and El-Khasawneh, B., “A novel walking parallel robot for on-structure three-axis machining of large structures,” J. Mech. Robot. 15(6), 061016 (2023).CrossRefGoogle Scholar
De Benedictis, C. and Ferraresi, C., “Precise positioning in a robotized laser-cutting machine allowed by a three-V-shaped-groove kinematic coupling: A feasibility study,” Robotica 41(9), 2703–2712 (2023).Google Scholar
Xie, Z., Xie, F., Liu, X.-J., Wang, J. and Shen, X., “Parameter optimization for the driving system of a 5 Degrees-of-freedom parallel machining robot with planar kinematic chains,” J. Mech. Robot. 11(4), 410–415 (2019).Google Scholar
Li, R., Cui, X. L., Lin, J. C. and Zheng, Y. H., “An attitude measurement method of industrial robots based on the inertial technology,” Robotica 40(8), 2909–2931 (2022).CrossRefGoogle Scholar
Xu, L., Chai, X. and Ding, Y., “Design and analysis of a reconfigurable hybrid robot for machining of large workpieces,” J. Mech. Robot. 16(5), 051001 (2023).CrossRefGoogle Scholar
Wu, J., Gao, Y., Zhang, B. B. and Wang, L. P., “Workspace and dynamic performance evaluation of the parallel manipulators in a spray-painting equipment,” Robot. Cim-INT Manuf. 44(1), 199–207 (2017).Google Scholar
Xiao, J. L., Zhao, S. L., Guo, H., Huang, T. and Lin, B., “Research on the collaborative machining method for dual-robot mirror milling,” Int. J. Adv. Manuf. Technol. 105(10), 4071–4084 (2019).CrossRefGoogle Scholar
Veeramani, S. and Muthuswamy, S., “Hybrid type multi-robot path planning of a serial manipulator and SwarmItFIX robots in sheet metal milling process,” Complex Intell. Syst. 8(4), 2937–2954 (2022).CrossRefGoogle Scholar
Zhao, X. W., Tao, B. and Ding, H., “Multimobile robot cluster system for robot machining of large-scale workpieces,” IEEE-ASME Trans. Mech. 27(1), 561–571 (2022).Google Scholar
Marvel, J. A., Bostelman, R. and Falco, J., “Multi-robot assembly strategies and metrics,” ACM Comput. Surv. 51(1), 1–32 (2018).Google ScholarPubMed
Dogar, M., Spielberg, A., Baker, S. and Rus, D., “Multi-robot grasp planning for sequential assembly operations,” Auton. Robot. 43(3), 649–664 (2019).CrossRefGoogle Scholar
Feng, Z., Hu, G. Q., Sun, Y. J. and Soon, J., “An overview of collaborative robotic manipulation in multi-robot systems,” Ann. Rev. Control 49(1), 113–127 (2020).Google Scholar
Boschetti, G., Bottin, M., Faccio, M. and Minto, R., “Multi-robot multi-operator collaborative assembly systems: A performance evaluation model,” J. Intell. Manuf. 32(5), 1455–1470 (2021).Google Scholar
Wu, J., Zhang, B. B., Wang, L. P. and Yu, G., “An iterative learning method for realizing accurate dynamic feedforward control of an industrial hybrid robot,” Sci. China Technol. Sci. 64(6), 1177–1188 (2021).CrossRefGoogle Scholar
Maghami, A. and Khoshdarregi, M., “Vision-based target localization and online error correction for high-precision robotic drilling,” Robotica 42(9), 3019–3043 (2024).CrossRefGoogle Scholar
Tao, Y., Chen, S., Liu, H. T., Wan, J. H., Wei, H. X. and Wang, T. M., “Robot hybrid inverse dynamics model compensation method based on the BLL residual prediction algorithm,” Robotica 1(1), 1–18 (2024).Google Scholar
Wu, J., Li, T. M., Wang, J. S. and Wang, L. P., “Stiffness and natural frequency of a 3-DOF parallel manipulator with consideration of additional leg candidates,” Robot. Auton. Syst. 61(8), 868–875 (2013).CrossRefGoogle Scholar
Wang, J., Wu, L., Meng, M. Q.-H. and Ren, H., Towards simultaneous coordinate calibrations for cooperative multiple robots, 410–415 (2014).CrossRefGoogle Scholar
Wu, L., Wang, J. L., Qi, L., Wu, K. Y., Ren, H. L. and Meng, M. Q. H., “Simultaneous hand-eye, tool-flange, and robot-robot calibration for comanipulation by solving the AXB = YCZ problem,” IEEE Trans. Robot. 32(2), 413–428 (2016).CrossRefGoogle Scholar
Sui, S. and Ding, Y., “Solving the AXB=YCZ problem for a dual-robot system with geometric calculus,” IEEE Trans. Autom. Sci. Eng. 21(3), 4627–4645 (2024).Google Scholar
Qiao, Y., Chen, Y. P., Chen, B. and Xie, J. M., “A novel calibration method for multi-robots system utilizing calibration model without nominal kinematic parameters,” Precis. Eng.-J. Int. Soc. Precis. Eng. Nanotechnol. 50(1), 211–221 (2017).Google Scholar
Nubiola, A. and Bonev, I. A., “Absolute calibration of an ABB IRB. 1600 robot using a laser tracker,” Robot. Cim-INT Manuf. 29(1), 236–245 (2013).CrossRefGoogle Scholar
Xu, X. H., Zhu, D. H., Zhang, H. Y., Yan, S. J. and Ding, H., “TCP-based calibration in robot-assisted belt grinding of aero-engine blades using scanner measurements,” Int J Adv Manuf Tech 90(1-4), 635–647 (2017).CrossRefGoogle Scholar
Wang, X., Ren, W., Wang, K., Liu, J., Lin, J., Feng, J., Zheng, J. and Li, F., “A self-calibration method for robot end-effector using spherical constraints,” Appl. Sci. 14(21), 9824 (2024).Google Scholar
Dehghani, M., McKenzie, R. A., Irani, R. A. and Ahmadi, M., “Robot-mounted sensing and local calibration for high-accuracy manufacturing,” Robot. Cim-INT Manuf. 79(1), 102429 (2023).CrossRefGoogle Scholar
Chen, T. H., Li, S., Qiao, Y. and Luo, X., “A robust and efficient ensemble of diversified evolutionary computing algorithms for accurate robot calibration,” IEEE Trans. Instrum. Meas. 73(1), 1–14 (2024).Google Scholar
Cao, H. Q., Nguyen, H. X., Tran, T. N. C., Tran, H. N. and Jeon, J. W., “A robot calibration method using a neural network based on a butterfly and flower pollination algorithm,” IEEE Trans. Ind. Electron. 69(4), 3865–3875 (2022).Google Scholar
Chen, X. Z. and Zhan, Q., “The kinematic calibration of an industrial robot with an improved beetle swarm optimization algorithm,” IEEE Robot. Autom. Lett. 7(2), 4694–4701 (2022).CrossRefGoogle Scholar
Zhong, H. R., Hu, C. H., Li, X. Y., Gao, L., Zeng, B. and Dong, H. Z., “Kinematic calibration method for a two-segment hydraulic leg based on an improved whale swarm algorithm,” Robot. Cim-INT Manuf. 59(1), 361–372 (2019).CrossRefGoogle Scholar
Zhao, W. G., Wang, L. Y., Zhang, Z. X., Fan, H. G., Zhang, J. J., Mirjalili, S., Khodadadi, N. and Cao, Q. J., “Electric EEL foraging optimization: A new bio-inspired optimizer for engineering applications,” Expert Syst. Appl. 238(1), 122200 (2024).Google Scholar