Abstract
Absolute accuracy of industrial robot is required for most of industrial applications. However, positioning errors of several millimeters are induced by many factors. Hybrid calibration, combining analytical model and learning-based regression, can compensate for most of the positioning error, including payload effects. However, when the payload changes, hybrid calibration has to be performed again. In this paper, hybrid calibration is applied on an industrial robot in two different sub-workspaces, with two different payloads. The results of this method have been compared to other calibration approaches, and highlight that hybrid calibration provides a higher final accuracy. Moreover, two data-efficient and pragmatic approaches are proposed, to address the issue of changing payload. Both methods are based on hybrid calibration. The first one uses previously-acquired knowledge to drastically reduce the number of measurements necessary to update a trained learning model with another payload. The second one uses a model trained separately for two different payloads and interpolates the outputs to compensate for new payloads without any additional measurement. The datasets used are available at: https://doi.org/10.57745/DWUC0H. The methods have been experimentally validated using a compensation algorithm and compared to other approaches, and show that the positioning error can be reduced by 95%.
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References
Elatta, A.Y., Gen, L.P., Zhi, F.L., Daoyuan, Y., Fei, L.: An overview of robot calibration. Inf. Technol. J. 3, 74–78 (2004)
Xuan, J.-Q., Xu, S.-H., et al.: Review on kinematics calibration technology of serial robots. Int. J. Precis. Eng. Manuf. 15(8), 1759–1774 (2014)
Mooring, B., Roth, Z., Driels, M.: Fundamentals of manipulator calibration, 27 (1991)
Li, Z., Li, S., Luo, X.: An overview of calibration technology of industrial robots. IEEE/CAA J. Automatica Sinica 8(1), 23–36 (2021)
J. Kenneth, S.: Active stiffness control of a manipulator in cartesian coordinates. 1980 19th IEEE conference on decision and control including the symposium on adaptive processes, 95–100 (1980)
Khalil, W., Besnard, S.: Geometric calibration of robots with flexible joints and links. J. Intell. Rob. Syst. 34, 357–379 (2002)
Dumas, C., Caro, S., Garnier, S., Furet, B.: Joint stiffness identification of six-revolute industrial serial robots. Robotics and Computer-Integrated Manufacturing 27(4), 881–888 (2011)
Olabi, A., Damak, M., Bearee, R., Gibaru, O., Leleu, S.: Improving the accuracy of industrial robots by offline compensation of joints errors. In: 2012 IEEE international conference on industrial technology, pp. 492–497 (2012)
Besset, P., Olabi, A., Gibaru, O.: Advanced calibration applied to a collaborative robot. In: 2016 IEEE International power electronics and motion control conference (PEMC), pp. 662–667 (2016)
Theissen, N.A., Laspas, T., Archenti, A.: Closed-force-loop elastostatic calibration of serial articulated robots. Robotics and Computer-Integrated Manufacturing 57, 86–91 (2019). https://doi.org/10.1016/j.rcim.2018.07.007
Tuttle, T.D., Seering, W.P.: A nonlinear model of a harmonic drive gear transmission. IEEE Trans. Robot. Autom. 12(3), 368–374 (1996). https://doi.org/10.1109/70.499819
Kircanski, N., Goldenberg, A.A., Jia, S.: An experimental study of nonlinear stiffness, hysteresis, and friction effects in robot joints with harmonic drives and torque sensors, vol. 16, pp. 326–340 (1993)
Klimchik, A., Pashkevich, A., Chablat, D.: Fundamentals of manipulator stiffness modeling using matrix structural analysis. Mech. Mach. Theory 133, 365–394 (2019). https://doi.org/10.1016/j.mechmachtheory.2018.11.023
Rezaei, A., Akbarzadeh, A.: Compliance error modeling for manipulators considering the effects of the component weights and the body and joint flexibilities. Mech. Mach. Theory 130, 244–275 (2018). https://doi.org/10.1016/j.mechmachtheory.2018.08.012
Klimchik, A., Pashkevich, A., Chablat, D.: Cad-based approach for identification of elasto-static parameters of robotic manipulators. Finite Elem. Anal. Des. 75, 19–30 (2013). https://doi.org/10.1016/j.finel.2013.06.008
Wang, Y.Y., Huang, T., Zhao, X.M., Mei, J.P., Chetwynd, D.G., Hu, S.J.: Finite element analysis and comparison of two hybrid robots-the tricept and the TriVariant, pp. 490–495 (2006). https://doi.org/10.1109/IROS.2006.282522, https://www.scopus.com/inward/record.uri?eid=2-s2.0-34250625329 &doi=10.1109%2fIROS.2006.282522 &partnerID=40 &md5=db128af8200a51e5aa64ac8c1f4f753b
Cao, W.-A., Yang, D., Ding, H.: A method for stiffness analysis of overconstrained parallel robotic mechanisms with scara motion. Robotics and Computer-Integrated Manufacturing 49, 426–435 (2018). https://doi.org/10.1016/j.rcim.2017.08.014
Kumar, P., et al.: Artificial neural network based geometric error correction model for enhancing positioning accuracy of a robotic sewing manipulator. Procedia Comput. Sci. 133, 1048–1055 (2018)
Takanashi, N.: 6 dof manipulators absolute positioning accuracy improvement using a neural-network. In: EEE International workshop on intelligent robots and systems, towards a new frontier of applications, pp. 635–6402 (1990)
Josin, G., Charney, D., White, D.: Robot Control Using Neural Networks. In: IEEE 1988 International conference on neural networks, pp. 625–631 (1988). https://doi.org/10.1109/icnn.1988.23980
Gao, G., Zhang, H., San, H., Wu, X., Wang, W.: Modeling and error compensation of robotic articulated arm coordinate measuring machines using bp neural network. Complexity 2017 (2017)
Aoyagi, S., Kohama, A., Nakata, Y., Hayano, Y., Suzuki, M.: Improvement of robot accuracy by calibrating kinematic model using a laser tracking system-compensation of non-geometric errors using neural networks and selection of optimal measuring points using genetic algorithm-, 5660–5665 (2010)
Nguyen, H.-N., Zhou, J., Kang, 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, 168781401882293 (2019)
Zhao, G., Zhang, P., Ma, G., Xiao, W.: System identification of the nonlinear residual errors of an industrial robot using massive measurements. Robotics and Computer-Integrated Manufacturing 59, 104–114 (2019)
Nguyen, H.X., Cao, H.Q., Nguyen, T.T., Tran, T.N.-C., Tran, H.N., Jeon, J.W.: Improving robot precision positioning using a neural network based on levenberg marquardt-apso algorithm. IEEE Access 9, 75415–75425 (2021). https://doi.org/10.1109/ACCESS.2021.3082534
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). https://doi.org/10.1016/j.ifacol.2020.12.1436
Hsiao, J.-C., Shivam, K., Lu, I.-F., Kam, T.-Y.: Positioning accuracy improvement of industrial robots considering configuration and payload effects via a hybrid calibration approach. IEEE Access 8, 228992–229005 (2020)
Sun, Y., Hollerbach, J.M.: Observability index selection for robot calibration. In: 2008 IEEE International conference on robotics and automation, pp. 831–836 (2008). IEEE
Joubair, A., Bonev, I.A.: Comparison of the efficiency of five observability indices for robot calibration. Mech. Mach. Theory 70, 254–265 (2013)
Klimchik, A., Wu, Y., Pashkevich, A., Caro, S., Furet, B.: Optimal selection of measurement configurations for stiffness model calibration of anthropomorphic manipulators. Appl. Mech. Mater. 162, 161–170 (2012)
Klimchik, A., Caro, S., Pashkevich, A.: Optimal pose selection for calibration of planar anthropomorphic manipulators. Precis. Eng. 40, 214–229 (2015)
Ye, C., Yang, J., Ding, H.: High-accuracy prediction and compensation of industrial robot stiffness deformation. Int. J. Mechanical Sci. 233 (2022). https://doi.org/10.1016/j.ijmecsci.2022.107638
Yu, W., Kumar, V.C.V., Turk, G., Liu, C.K.: Sim-to-real transfer for biped locomotion, pp. 3503–3510 (2019). https://doi.org/10.1109/IROS40897.2019.8968053
Selingue, M., Olabi, A., Thiery, S., Béarée, R.: Experimental analysis of robot hybrid calibration based on geometrical identification and artificial neural network. In: IECON 2022 - 48th annual conference of the ieee industrial electronics society, pp. 1–6 (2022). https://doi.org/10.1109/IECON49645.2022.9968704
Denavit, J., Hartenberg, R.S.: Notation for lower-pair mechanisms based on matrices. A Kinematic,. ASME J. Appl. Mech. 22, 215–221 (1995)
Siciliano, B., Khatib, O.: Springer Handbook of Robotics. Springer, Berlin, Heidelberg (2007)
Khalil, W., Dombre, E.: Chapter 11 - geometric calibration of robots. In: Khalil, W., Dombre, E. (eds.) Modeling, Identification and Control of Robots, pp. 257–289. Butterworth-Heinemann, Oxford (2002)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Teh, Y.W., Titterington, M. (eds.) Proceedings of the thirteenth international conference on artificial intelligence and statistics. Proceedings of machine learning research, vol. 9, pp. 249–256. PMLR, Chia Laguna Resort, Sardinia, Italy (2010). https://proceedings.mlr.press/v9/glorot10a.html
Meng, Y., Zhuang, H.: Autonomous robot calibration using vision technology. Robotics and Computer-Integrated Manufacturing 23(4), 436–446 (2007)
Mazzoni, F., Olabi, A., Bearee, R., Ernst-Desmulier, J.-B.: Calibration methodology for multirobot assembly cell. In: IECON 2022 - 48th annual conference of the IEEE industrial electronics society, pp. 1–5 (2022). https://doi.org/10.1109/IECON49645.2022.9968339
Dombre, E., Khalil, W.: Robot Manipulators: Modeling. Performance Analysis and Control. Control Systems, Robotics and Manufacturing Series (2007)
Gong, C., Yuan, J., Ni, J.: Nongeometric error identification and compensation for robotic system by inverse calibration. Int. J. Mach. Tools Manuf 40(14), 2119–2137 (2000). https://doi.org/10.1016/S0890-6955(00)00023-7
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Maxime Selingue. The first draft of the manuscript was written by Maxime Selingue and all authors commented on previous versions of the manuscript.
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Selingue, M., Olabi, A., Thiery, S. et al. Hybrid Calibration of Industrial Robot Considering Payload Variation. J Intell Robot Syst 109, 58 (2023). https://doi.org/10.1007/s10846-023-01980-6
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DOI: https://doi.org/10.1007/s10846-023-01980-6