Hostname: page-component-6766d58669-bp2c4 Total loading time: 0 Render date: 2026-05-18T02:23:14.579Z Has data issue: false hasContentIssue false
  • English
  • Français

High accuracy hybrid kinematic modeling for serial robotic manipulators

Published online by Cambridge University Press:  19 September 2024

Marco Ojer Open the ORCID record for Marco Ojer [Opens in a new window] ,
Ander Etxezarreta ,
Gorka Kortaberria ,
Brahim Ahmed ,
Jon Flores ,
Javier Hernandez ,
Elena Lazkano  and
Xiao Lin
Marco Ojer*
Affiliation:
Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastian, Spain University of the Basque Country (UPV/EHU), Donostia-San Sebastian, Spain
Ander Etxezarreta
Affiliation:
Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastian, Spain
Gorka Kortaberria
Affiliation:
Tekniker Research Centre, Basque Research and Technology Alliance (BRTA) Eibar, Eibar, Spain
Brahim Ahmed
Affiliation:
Tekniker Research Centre, Basque Research and Technology Alliance (BRTA) Eibar, Eibar, Spain
Jon Flores
Affiliation:
Tekniker Research Centre, Basque Research and Technology Alliance (BRTA) Eibar, Eibar, Spain
Javier Hernandez
Affiliation:
Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastian, Spain
Elena Lazkano
Affiliation:
University of the Basque Country (UPV/EHU), Donostia-San Sebastian, Spain
Xiao Lin
Affiliation:
Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastian, Spain
*
Corresponding author: Marco Ojer; Email: mojer@vicomtech.org
Get access Rights & Permissions [Opens in a new window]

Abstract

In this study, we present a hybrid kinematic modeling approach for serial robotic manipulators, which offers improved accuracy compared to conventional methods. Our method integrates the geometric properties of the robot with ground truth data, resulting in enhanced modeling precision. The proposed forward kinematic model combines classical kinematic modeling techniques with neural networks trained on accurate ground truth data. This fusion enables us to minimize modeling errors effectively. In order to address the inverse kinematic problem, we utilize the forward hybrid model as feedback within a non-linear optimization process. Unlike previous works, our formulation incorporates the rotational component of the end effector, which is beneficial for applications involving orientation, such as inspection tasks. Furthermore, our inverse kinematic strategy can handle multiple possible solutions. Through our research, we demonstrate the effectiveness of the hybrid models as a high-accuracy kinematic modeling strategy, surpassing the performance of traditional physical models in terms of positioning accuracy.

Keywords

topological modeling of robotsrobot dynamicsmanufacturingautomationserial manipulator design and kinematics

Information

Type
Research Article
Information
Robotica , Volume 42 , Issue 9 , September 2024 , pp. 3211 - 3229
Copyright
© The Author(s), 2024. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Aiman, M., Bahrin, K., Othman, F., Hayati, N., Azli, N. and Talib, F., “Industry 4.0: A review on industrial automation and robotics,” J Teknol 78, 2180–3722 (2016).Google Scholar
Robert, B., “The growing use of robots by the aerospace industry,” Ind Robot An Int J 45(6), 705–709 (2018).Google Scholar
Hassan, A., El-Habrouk, A., Deghedie, M. and Deghedie, S., “Renewable energy for robots and robots for renewable energy - a review,” Robotica 38, 1–29 (2019).Google Scholar
Kyrarini, M., Lygerakis, F., Rajavenkatanarayanan, A., Sevastopoulos, C., Nambiappan, H. R., Chaitanya, K. K., Babu, A. R., Mathew, J. and Makedon, F., “Survey of robots in healthcare,” Technologies 9(1), 8 (2021).CrossRefGoogle Scholar
Urhal, P., Weightman, A., Diver, C. and Bartolo, P., “Robot assisted additive manufacturing: A review,” Robot Comp Int Manuf 59, 335–345 (2019).CrossRefGoogle Scholar
Veitschegger, W. and Wu, C. H., “Robot accuracy analysis based on kinematics,” IEEE J Robot Autom 2(3), 171–179 (1986).CrossRefGoogle Scholar
Motta, J., Calibration: Modeling measurement and applications, (2006).Google Scholar
Sirinterlikci, A., Tiryakioglu, M., Bird, A., Harris, A. and Kweder, K., ”Repeatability and accuracy of an industrial robot: Laboratory experience for a design of experiments course,” Technol Interf J 9 (2009).Google Scholar
Bernard, R., Calibration First Edition (Springer, US, 1993).Google Scholar
Ginani, L. and Motta, J., “Theoretical and practical aspects of robot calibration with experimental verification,” J Braz Soc Mech Sci Eng 33(1), 15–21 (2011).CrossRefGoogle Scholar
Roth, Z., Mooring, B. and Ravani, B., “An overview of robot calibration,” IEEE J Robot Autom 3(5), 377–385 (1987).CrossRefGoogle Scholar
Brahmia, A., Kerboua, A., Kelaiaia, R. and Latreche, A., ”Tolerance synthesis of delta-like parallel robots using a nonlinear optimisation method,” Appl Sci 13, 10703 (2023).CrossRefGoogle Scholar
Shi, X., Zhang, F., Qu, X. and Liu, B., “An online real-time path compensation system for industrial robots based on laser tracker,” Int J Adv Robot Syst 13(5), 172988141666336 (2016).CrossRefGoogle Scholar
Vincze, M., Spiess, S., Parotidis, M. and Götz, M., “Automatic generation of nonredundant and complete models for geometric and non-geometric errors of robots,” Int J Model Simul 19(3), 236–243 (1999).CrossRefGoogle Scholar
Braun, T., Embedded Robotic: Robot Manipulators (Springer, Singapore, 2022) pp. 253–269.CrossRefGoogle Scholar
Slavkovic, N., Milutinovic, D., Kokotovic, B., Glavonjic, M. M., Zivanovic, S. and Ehmann, K. F., ” Cartesian compliance identification and analysis of an articulated machining robot,” FME Trans 41, 83–95 (2013).Google Scholar
Dumas, C., Caro, S., Garnier, S. and Furet, B., “Joint stiffness identification of industrial serial robots,” Robot Comp Integ Manuf 27(4), 881–888 (2011).CrossRefGoogle Scholar
Kamali, K., Joubair, A., Bonev, I. A. and Bigras, P., “Elasto-Geometrical Calibration of an Industrial Robot Under Multidirectional External Loads using a Laser Tracker,” In: IEEE International Conference on Robotics and Automation (ICRA), (2016) pp. 4320–4327.Google Scholar
Mendikute, A., Yagüe-Fabra, J. A., zatarain, M., Bertelsen, A. and Leizea, I., “Selfcalibrated in-process photogrammetry for large raw part measurement and alignment before machining,” Sensors 17(9), 2066 (2017).CrossRefGoogle ScholarPubMed
Morell, A., Tarokh, M. and Acosta, L., “Inverse Kinematics Solutions for Serial Robots Using Support Vector Regression,” In: IEEE International Conference on Robotics and Automation, (2013) pp. 4203–4208.Google Scholar
Morell, A., Tarokh, M. and Acosta, L., “Solving the forward kinematics problem in parallel robots using support vector regression,” Eng Appl Artif Intel 26(7), 1698–1706 (2013).CrossRefGoogle Scholar
Chen, D., Hu, F., Nian, G. and Yang, T., “Deep residual learning for nonlinear regression,” Entropy 22(2), 193 (2020).CrossRefGoogle ScholarPubMed
Theofanidis, M., Sayed, S. I., Cloud, J., Brady, J. and Makedon, F., “Kinematic Estimation with Neural Networks for Robotic Manipulators,” In: International Conference on Artificial Neural Networks, (2018) pp. 795–802.Google Scholar
Chen, X., Zhang, Q., Sun, Y. and Crippa, P., “Evolutionary robot calibration and nonlinear compensation methodology based on GA-DNN and an extra compliance error model,” Math Prob Eng 2020, 1–15 (2020).Google Scholar
Nguyen, H. N., Zhou, J. and 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(3), 996–1005 (2015).CrossRefGoogle Scholar
Wang, Z., Chen, Z., Wang, Y., Mao, C. and Hang, Q., “A robot calibration method based on joint angle division and an artificial neural network,” Math Probl Eng 2019,1–12 (2019).Google Scholar
Shah, S. K., Mishra, R. and Ray, L. S., “Solution and validation of inverse kinematics using deep artificial neural network,” Mater Today Proc 26, 1250–1254 (2020).CrossRefGoogle Scholar
Duka, A.-V., “Neural network based inverse kinematics solution for trajectory tracking of a robotic arm,” Proc Technol 12, 20–27 (2014).CrossRefGoogle Scholar
Srisuk, P., Sento, A. and Kitjaidure, Y., “Inverse Kinematics Solution Using Neural Networks from Forward Kinematics Equations,” In: 9th International Conference on Knowledge and Smart Technology (KST), (2017) pp. 61–65.Google Scholar
Amusawi, A. R., Dülger, L. C. and Kapucu, S., “A new artificial neural network approach in solving inverse kinematics of robotic arm (denso vp6242),” Comput Intel Neurosc 2016, 1–10 (2016).CrossRefGoogle Scholar
Costa, P., Lima, J., Pereira, A. and Pinto, A., “An Optimization Approach for the Inverse Kinematics of a Highly Redundant Robot,” In: Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA, (2015) pp. 433–442.Google Scholar
Bruyninckx, H., Soetens, P. and Koninckx, B., “The Real-Time Motion Control Core of the Orocos Project,” In: IEEE International Conference on Robotics and Automation, (2003) pp. 2766–2771.Google Scholar
Corke, P., “Robotics, vision and control - fundamental algorithms in MATLAB,” Springer Tracts Adv Robot 73, 1–495 (2011).CrossRefGoogle Scholar
Han, D.-P., Wei, Q. and Li, Z.-X., “Kinematic control of free rigid bodies using dual quaternions,” Int J Autom Comp 5(3), 319–324 (2008).CrossRefGoogle Scholar
Beeson, P. and Ames, B., “Trac-ik: An Open-Source Library for Improved Solving of Generic Inverse Kinematics,” In: IEEE RAS Humanoids Conference (2015) pp. 928–935.Google Scholar
Maceron, P., “IKPy,” doi: 10.5281/zenodo.6551158 (2015).CrossRefGoogle Scholar
Nubiola, A. and Bonev, I. A., “Absolute calibration of an ABB IRB. 1600 robot using a laser tracker,” Robot Com Int Manuf 29(1), 236–245 (2013).CrossRefGoogle Scholar
Lattanzi, L., Cristalli, C., Massa, D., Boria, S., Lepine, P. and Pellicciari, M., “Geometrical calibration of a 6-axis robotic arm for high accuracy manufacturing task,” Int J Adv Manuf Technol 111(7), 1813–1829 (2020).CrossRefGoogle Scholar