Abstract
In this work, a novel model-free robotic residual errors compensation method is proposed based on the information-diffusion-based dataset enhancement (ID-DE) and the Gradient-Boosted Decision Trees (GBDT). Firstly, the dataset enhancement method is developed by utilizing the normal membership function based on the information diffusion technology. Then, merging it with multiple GBDTs, the multi-output residual errors learning model (ID-GBDTs) is constructed, and the grid search is used to determine the optimal hyper-parameters to accomplish the accurate prediction of residual errors. Finally, the compensation of robotic residual errors is realized by using the calibrated kinematic model. Experiments show that ID-DE can significantly improve the generalization ability of various learning models on the few-shot dataset. The R-squared of ID-GBDTs is improved from 0.58 to 0.77 along with the MAE decreased from 0.23 to 0.16, compared to original GBDT. Through the compensation of the residual errors, the mean/maximum absolute positioning error of the UR10 robot are optimized from 4.51/9.42 mm to 0.81/2.65 mm, with an accuracy improvement of 82.03%.
Supported by the National Key R &D Program of China (No. 2019YFA0706703), the National Nature Science Foundation of China (Nos. 52075204, 52105514) and the China Scholarship Council (No. 202106160036).
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References
Xie, H.L., Wang, Q.H., Ong, S.K., Li, J.R., Chi, Z.P.: Adaptive human-robot collaboration for robotic grinding of complex workpieces. CIRP Ann. (2022). https://doi.org/10.1016/j.cirp.2022.04.015
Yang, Z., et al.: Prediction and analysis of material removal characteristics for robotic belt grinding based on single spherical abrasive grain model. Int. J. Mech. Sci. 190, 106005 (2021)
Peng, J., Ding, Y., Zhang, G., Ding, H.: An enhanced kinematic model for calibration of robotic machining systems with parallelogram mechanisms. Robot. Comput.-Integr. Manuf. 59, 92–103 (2019)
Sun, T., Liu, C., Lian, B., Wang, P., Song, Y.: Calibration for precision kinematic control of an articulated serial robot. IEEE Trans. Industr. Electron. 68(7), 6000–6009 (2021)
Zhuang, H., Wang, L.K., Roth, Z.S.: Error-model-based robot calibration using a modified CPC model. Robot. Comput.-Integr. Manuf. 10(4), 289–299 (1993)
Li, Z., Li, S., Luo, X.: Data-driven industrial robot arm calibration: a machine learning perspective. In: 2021 IEEE International Conference on Networking, Sensing and Control (ICNSC), vol. 1, pp. 1–6 (2021)
Li, Z., Li, S., Bamasag, O.O., Alhothali, A., Luo, X.: Diversified regularization enhanced training for effective manipulator calibration. IEEE Trans. Neural Netw. Learn. Syst. 1–13 (2022). https://doi.org/10.1109/TNNLS.2022.3153039
Landgraf, C., Ernst, K., Schleth, G., Fabritius, M., Huber, M.F.: A hybrid neural network approach for increasing the absolute accuracy of industrial robots. In: 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE), pp. 468–474 (2021). https://doi.org/10.1109/CASE49439.2021.9551684
Chen, X., Zhang, Q., Sun, Y.: Evolutionary robot calibration and nonlinear compensation methodology based on GA-DNN and an extra compliance error model. Math. Probl. Eng. 2020, 3981081 (2020)
Zhao, G., Zhang, P., Ma, G., Xiao, W.: System identification of the nonlinear residual errors of an industrial robot using massive measurements. Robot. Comput. Integr. Manuf. 59, 104–114 (2019)
Wang, W., Tian, W., Liao, W., Li, B.: Pose accuracy compensation of mobile industry robot with binocular vision measurement and deep belief network. Optik 238, 166716 (2021)
Chen, D., Wang, T., Yuan, P., Sun, N., Tang, H.: A positional error compensation method for industrial robots combining error similarity and radial basis function neural network. Meas. Sci. Technol. 30(12), 125010 (2019)
Gao, G., Liu, F., San, H., Wu, X., Wang, W.: Hybrid optimal kinematic parameter identification for an industrial robot based on BPNN-PSO. Complexity 2018, 4258676 (2018)
Gadringer, S., Gattringer, H., Müller, A., Naderer, R.: Robot calibration combining kinematic model and neural network for enhanced positioning and orientation accuracy. IFAC-PapersOnLine 53(2), 8432–8437 (2020)
Huang, C.: Principle of information diffusion. Fuzzy Sets Syst. 91(1), 69–90 (1997)
Li, D.C., Wu, C.S., Tsai, T.I., Lina, Y.S.: Using mega-trend-diffusion and artificial samples in small data set learning for early flexible manufacturing system scheduling knowledge. Comput. Oper. Res. 34(4), 966–982 (2007)
Zhang, Z., Jung, C.: GBDT-MO: gradient-boosted decision trees for multiple outputs. IEEE Trans. Neural Netw. Learn. Syst. 32(7), 3156–3167 (2020)
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
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Yang, Z., Xu, X., Li, C., Yan, S., Ge, S.S., Ding, H. (2022). Information Diffusion for Few-Shot Learning in Robotic Residual Errors Compensation. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13455. Springer, Cham. https://doi.org/10.1007/978-3-031-13844-7_59
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DOI: https://doi.org/10.1007/978-3-031-13844-7_59
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