Abstract
The low absolute positioning accuracy of industrial robots leads to low machining accuracy, seriously hindering the development and application of robots in the field of high-precision machining. To solve this problem, this article proposes a robot machining positioning error modeling and compensation method based on RVM. This method takes advantage of RVM’s adaptive parameters and high sparsity to identify the strong nonlinear positioning error. Then, based on the error model, the iterative compensation problem is transformed into an optimization problem to reduce the number of inverse kinematic computations of the robot. Finally, through experiments, the effectiveness of positioning error prediction and compensation is demonstrated.
This work is co-supported by the National Key R &D Program of China [Grant No. 2022YFB4702500], and the National Natural Science Foundation of China [Grant No. U22A20176], the Guangdong HUST Industrial Technology Research Institute, Guangdong Provincial Key Laboratory of Manufacturing Equipment Digitization [Grant NO. 2020B1212060014], the Guangdong Basic and Applied Basic Research Foundation [Grant No. 2021A1515110898], the GDAS’ Project of Science and Technology Development [Grant No. 2020GDASYL-20200202001].
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kai, W., Li, J., Zhao, H., Zhong, Y.: Review of industrial robot stiffness identification and modelling. Appl. Sci. 12(17), 8719 (2022)
Xie, H., Li, W.L., Zhu, D., Yin, Z., Ding, H.: A systematic model of machining error reduction in robotic grinding. IEEE/ASME Trans. Mechatron. PP(99), 1 (2020)
Ye, C., Yang, J., Zhao, H., Ding, H.: Task-dependent workpiece placement optimization for minimizing contour errors induced by the low posture-dependent stiffness of robotic milling. Int. J. Mech. Sci. 205, 106601 (2021)
Nubiola, A., Bonev, I.A.: Absolute calibration of an ABB IRB 1600 robot using a laser tracker. Robot. Comput. Integr. Manufact. 29(1), 236–245 (2013)
Hu, J., Hua, F., Tian, W.: Robot positioning error compensation method based on deep neural network. J. Phys: Conf. Ser. 1487, 012045 (2020)
Ma, L., Bazzoli, P., Sammons, P.M., Landers, R.G., Bristow, D.A.: Modeling and calibration of high-order joint-dependent kinematic errors for industrial robots. Robot. Comput.-Integrat. Manufact. 50, S0736584517301965 (2017)
Alici, G., Shirinzadeh, B.: A systematic technique to estimate positioning errors for robot accuracy improvement using laser interferometry based sensing. Mech. Machine Theory 40(8), 879–906 (2005)
Xu, W.L., Wurst, K.H., Watanabe, T., Yang, S.Q.: Calibrating a modular robotic joint using neural network approach. In: IEEE World Congress on IEEE International Conference on Neural Networks (1994)
Nguyen, A.H.N., Zhou, A.J., Kang, B.H.J.: A calibration method for enhancing robot accuracy through integration of an extended Kalman filter algorithm and an artificial neural network. Neurocomputing 151, 996–1005 (2015)
Nguyen, H.N., Le, P.N., Kang, H.J.: 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), 168781401882293 (2019)
Li, B., Tian, W., Zhang, C., Hua, F., Li, Y.: Positioning error compensation of an industrial robot using neural networks and experimental study. Chin. J. Aeronautics 35(3), 346–360 2021
Zhou, W.: Theory and experiment of industrial robot accuracy compensation method based on spatial interpolation. J. Mech. Eng. 49(3), 7 (2013)
Wang, W., Tian, W., Liao, W., Li, B., Hu, J.: Error compensation of industrial robot based on deep belief network and error similarity. Robot. Comput.-Integr. Manufact. 73(8), 102220 (2022)
Luo, G., Zou, L., Wang, Z., Lv, C., Huang, Y.: A novel kinematic parameters calibration method for industrial robot based on Levenberg-Marquardt and differential evolution hybrid algorithm. Robot. Comput.-Integr. Manufact. 71(1), 102165 (2021)
Dongdong, C., Peijiang, Y., Tianmiao, W., Ying, C., Lei, X.: A compensation method for enhancing aviation drilling robot accuracy based on co-kriging. Int. J. Precis. Eng. Manuf. 19(8), 1133–1142 (2018)
Gong, C., Yuan, J., Ni, J.: Nongeometric error identification and compensation for robotic system by inverse calibration. Int. J. Mach. Tools Manufact. 40(14), 2119–2137 (2000)
Ma, S., Deng, K., Lu, Y., et al.: Robot error compensation based on incremental extreme learning machines and an improved sparrow search algorithm. Int. J. Adv. Manuf. Technol. 125, 5431–5443 (2023)
Zhijun, W., Gao, P., Cui, L., Chen, J.: An incremental learning method based on dynamic ensemble RVM for intrusion detection. IEEE Trans. Netw. Serv. Manage. 19(1), 671–685 (2022)
Nao, S., Wang, Y.: Fault detection of gearbox by multivariate extended variational mode decomposition-based time-frequency images and incremental RVM algorithm. Sci. Rep. 13(1), 7950 (2023)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wu, J., Liao, Z., Wu, H., Jiang, L., Sun, K. (2023). Positioning Error Modelling and Compensation Method for Robot Machining Based on RVM. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14272. Springer, Singapore. https://doi.org/10.1007/978-981-99-6480-2_32
Download citation
DOI: https://doi.org/10.1007/978-981-99-6480-2_32
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-6479-6
Online ISBN: 978-981-99-6480-2
eBook Packages: Computer ScienceComputer Science (R0)