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Study of a global calibration method for a planar parallel robot mechanism considering joint error

Published online by Cambridge University Press:  16 September 2024

Qinghua Zhang Open the ORCID record for Qinghua Zhang [Opens in a new window] ,
Huaming Yu ,
Lingbo Xie ,
Qinghua Lu  and
Weilin Chen
Qinghua Zhang
Affiliation:
School of Mechatronics Engineering and Automation, Foshan University, Foshan, China Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology, Foshan University, Foshan, China
Huaming Yu
Affiliation:
School of Mechatronics Engineering and Automation, Foshan University, Foshan, China Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology, Foshan University, Foshan, China
Lingbo Xie
Affiliation:
School of Mechatronics Engineering and Automation, Foshan University, Foshan, China Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology, Foshan University, Foshan, China
Qinghua Lu*
Affiliation:
School of Mechatronics Engineering and Automation, Foshan University, Foshan, China Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology, Foshan University, Foshan, China
Weilin Chen
Affiliation:
School of Mechatronics Engineering and Automation, Foshan University, Foshan, China Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology, Foshan University, Foshan, China
*
Corresponding author: Qinghua Lu; Email: qhlu@fosu.edu.cn
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Abstract

In order to improve the positioning accuracy of industrial robots, this paper proposes a global calibration method for planar parallel robot considering joint errors, which solves the problem that the existing calibration methods only consider part of the error sources and the calibration accuracy is poor, and improves the calibration efficiency and robot positioning accuracy. Consequently, it improves calibration efficiency and the overall precision of robot positioning. Firstly, the error model of overdetermined equations combined with structural parameters is established, and the global sensitivity of each error source is analyzed. Based on the measurement data of laser tracker, the local error source is identified by the least square method, which improves the local error accuracy by 88.6%. Then, a global error spatial interpolation method based on inverse distance weighting method is proposed, and the global accuracy is improved by 59.16%. Finally, the radial basis function neural network error prediction model with strong nonlinear approximation function is designed for global calibration, and the accuracy is improved by 63.05%. Experimental results verify the effectiveness of the proposed method. This study not only provides technical support for the engineering application of this experimental platform but also provides theoretical guidance for the improvement of the accuracy of related robot platforms.

Keywords

planar parallel robotpositioning accuracyinverse distance weightingradial basis function neural networkglobal calibration method
Type
Research Article
Information
Robotica , First View , pp. 1 - 30
Copyright
© The Author(s), 2024. Published by Cambridge University Press

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References

Tsai, C., Yu, C., Yeh, P. and Lan, C., “Parametric joint compliance analysis of a 3-UPU parallel robot,” Mech Mach Theory 170, 104721 (2022).Google Scholar
Olsson, T., Haage, M., Kihlman, H., Johansson, R., Nilsson, K., Robertsson, A., Björkman, M., Isaksson, R., Ossbahr, G. and Brogårdh, T., “Cost-efficient drilling using industrial robots with high-bandwidth force feedback,” Robot Comp-Int Manuf 26(1), 24–38 (2010).Google Scholar
Kelaiaia, R., Chemori, A., Brahmia, A., Kerboua, A., Zaatri, A. and Company, O., “Optimal dimensional design of parallel manipulators with an illustrative case study: A review, “Mech Mach Theory 188, 105390 (2023).Google Scholar
Chen, G., Kong, L., Li, Q. and Wang, H., “A simple two-step geometric approach for the kinematic calibration of the 3-PRS parallel manipulator,” Robotica 37(5), 837–850 (2019).Google Scholar
Gao, W., Huang, Q., Luo, R. and Zhang, Y., “An improved minimal error model for the robotic kinematic calibration based on the POE formula,” Robotica 40(5), 1607–1626 (2022).Google Scholar
Li, Z., Li, S. and Luo, X., “Efficient industrial robot calibration via a novel unscented kalman filter-incorporated variable step-size Levenberg-Marquardt algorithm,” IEEE Trans Instru Meas 72, 1–12 (2023).Google Scholar
Cao, H., Nguyen, H., Tran, T., Tran, H. and Jeon, J., “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
Sun, T., Lian, B., Zhang, J. and Song, Y., “Kinematic calibration of a 2-doF over-constrained parallel mechanism using real inverse kinematics,” IEEE Access 6, 67752–67761 (2018).Google Scholar
Brahmia, A., Kelaiaia, R., Company, O. and Chemori, A., “Kinematic sensitivity analysis of manipulators using a novel dimensionless index,” Robot Auto Syst 150, 104021 (2022).Google Scholar
Ye, H., Wu, J. and Huang, T., “Kinematic calibration of over-constrained robot with geometric error and internal deformation,” Mech Mach Theory 185, 105345 (2023).Google Scholar
Luo, X., Xie, F., Liu, X. and Xie, Z., “Kinematic calibration of a 5-axis parallel machining robot based on dimensionless error mapping matrix,” Robot Comp-Int Manuf 70, 102115 (2021).Google Scholar
Brahmia, A., Kerboua, A., Kelaiaia, R. and Latreche, A., “Tolerance synthesis of delta-like parallel robots using a nonlinear optimisation method,” Applied Sciences 13(19), 10703 (2023).Google Scholar
Ye, H., Wu, J. and Wang, D., “A general approach for geometric error modeling of over-constrained hybrid robot,” Mech Mach Theory 176, 104998 (2022).Google Scholar
Li, F., Zeng, Q., Ehmann, K. F., Cao, J. and Li, T., “A calibration method for overconstrained spatial translational parallel manipulators,” Robot Comp-Int Manuf 57, 241–254 (2019).Google Scholar
Chen, X., Zhang, Q. and Sun, Y., “Non-kinematic calibration of industrial robots using a rigid-flexible coupling error mo-del and a full pose measurement method,” Robot Comp-Int Manuf 57, 46–58 (2019).Google Scholar
Zhu, W., Qu, W., Cao, L., Yang, D. and Ke, Y., “An off-line programming system for robotic drilling in aerospace manufacturing,” Int J Adv Manuf Tech 68(9-12), 2535–2545 (2013).Google Scholar
Li, B., Tian, W., Zhang, C., Hua, F., Cui, G. and Li, Y., “Positioning error compensation of an industrial robot using neural networks and experimental study,” Chinese J Aeron 35(2), 346–360 (2022).Google Scholar
Liu, H., Yan, Z. and Xiao, J., “Pose error prediction and real-time compensation of a 5-DOF hybrid robot,” Mech Mach Theory 170, 104737 (2022).Google Scholar
Nguyen, H., Le, P. and Kang, H., “A new calibration method for enhancing robot position accuracy by combining a robot model–based identification approach and an artificial neural network–based error compensation technique,” Adv Mech Eng 11(1), 2072051381 (2019). Google Scholar
Bo, L., Wei, T., Chufan, Z., Fangfang, H., Guangyu, C. and Yufei, L., “Positioning error compensation of an industrial robot using neural networks and experim-ental study,” Chinese J Aeronaut 35(2), 346–360 (2022).Google Scholar
Miao, Y., Zhijiang, D., Lining, S. and Wei, D., “Optimal design, modeling and control of a long stroke 3-PRR comcspliant parallel manipulator with variable thickness flexure pivots,” Robot Comp Int Manuf 60, 23–33 (2019).Google Scholar
Huo, X., Lian, B., Wang, P., Song, Y. and Sun, T., “Dynamic identification of a tracking parallel mechanism,” Mech Mach Theory 155, 104091 (2020).Google Scholar
Naderi, D., Tale-Masouleh, M. and Varshovi-Jaghargh, P., “Gröbner basis and resultant method for the forward displace-ment of 3-doF planar parallel manipulators in seven-dimensional kinematic space,” Robotica 34(11), 2610–2628 (2016).Google Scholar
si Mo, J., Research On Plane 3-PRR Parallel Positioning System under Micro-Nano Operating Environment (South China University of Technology, 2016).Google Scholar
Zhao, Y., “Singularity, isotropy, and velocity transmission evaluation of a three translational degrees-of-freedom parallel robot, “Robotica 31(2), 193–202 (2013).Google Scholar
Ning, Y., Li, T., Du, W., Yao, C., Zhang, Y. and Shao, J., “Inverse kinematics and planning/control co-design method of redundant manipulator for precision operation: Design and experiments,” Robot Comp-Int Manuf 80, 102457 (2023).Google Scholar
Xie, L.-B., Qiu, Z.-C. and Zhang, X.-M., “Development of a 3-PRR precision tracking system with full closed-loop measureme-nt and control,” Sensors 19(8), 1756 (2019).Google ScholarPubMed
Chen, Y., He, K., Jiang, M., Wang, X. and Xie, L., “Kinematic calibration and feedforward control of a heavy-load manipulator using parameters optimization by an ant colony algorithm,” Robotica 1–29 (2024).Google Scholar
Zhao, X. and Luan, Q., “Kinematic modeling and error analysis of the 3-RRRU parallel robot,” Mech Design Manuf (1), 274–276 (2020).Google Scholar
Miranda-Colorado, R. and Moreno-Valenzuela, J., “Experimental parameter identification of flexible joint robot manipulators,” Robotica 36(3), 313–332 (2018).Google Scholar
Sourabh, K. and J.C., T., “Forward kinematics solution for a general Stewart platform through iteration based simulation,” Int J Adv Manuf 126(1-2), 813–825 (2023).Google Scholar
Rui, Q., Huijie, H., Keke, X., Bingchuan, L., Sha, L., Jingping, H., Shijie, B. and Jiakuan, Y., “Prediction on the combined toxicities of stimulation-only and inhibition-only contaminants using improved inverse distance weighted interpolation,” Chemosphere 287(Pt 3), 132045 (2021).Google Scholar
Eduardo, C. B., Luis, G. L. and Marcela, B. G., “Geometric techniques for robotics and HMI: Interpolation and haptics in conformal geometric algebra and control using quaternion spike neural networks[J],” Robot Auton Syst 104, 72–84 (2017).Google Scholar
Zhang, X. and Zeng, L., “Considered the kinematic calibration of the 3-RRR parallel mechanism with the back gap of the reducer [J],” J South China Univ Tech 44(7), 47–54 (2016).Google Scholar
Tong, L., Sheng, C., Po, Y., Yunpeng, Z. and Harris, C. J., “Efficient adaptive deep gradient RBF network for multi-output nonlinear and nonstationa-ry industrial processes,” J Process Contr 126, 1–11 (2023).Google Scholar
Eva, C., Harris, G., Nikos, P. and Theodoridis, T., “Particle swarm optimization and RBF neural networks for public transport arrival time prediction using GTFS data,” Int J Inform Manage Data Insigh 2(2), 10086 (2022).Google Scholar
Devesh, M. and Amrita, C., Reuse Estimate and Interval Prediction Using MOGA-NN and RBF-NN in the Functional Paradigm (Science of Computer Programming, 2021).Google Scholar
Guosheng, L., Shuaichao, M., Dequan, Z., Leping, Y., Weihua, Z. and Zeping, W., “An efficient sequential anisotropic RBF reliability analysis method with fast cross-validation and parallelizability,” Reliab Eng Syst Safe 241, 1–14 (2024).Google Scholar
Dongyang, H., Jinshi, C., Han, Z., Yiran, S. and Tongyang, W., “Intelligent prediction for digging load of hydraulic excavators based on RBF neural network,” Measurement 206, 112210 (2023).Google Scholar