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An online payload identification method based on parameter difference for industrial robots

Published online by Cambridge University Press:  13 September 2024

Tian Xu Open the ORCID record for Tian Xu [Opens in a new window] ,
Hua Tuo ,
Qianqian Fang Open the ORCID record for Qianqian Fang [Opens in a new window] ,
Jie Chen ,
Jizhuang Fan ,
Debin Shan  and
Jie Zhao
Tian Xu
Affiliation:
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China School of Materials Science and Engineering, Harbin Institute of Technology, Harbin, China
Hua Tuo
Affiliation:
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China
Qianqian Fang
Affiliation:
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China
Jie Chen*
Affiliation:
School of Mechanical Engineering and Automation, Northeastern University, Shenyang, China
Jizhuang Fan*
Affiliation:
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China
Debin Shan
Affiliation:
School of Materials Science and Engineering, Harbin Institute of Technology, Harbin, China
Jie Zhao*
Affiliation:
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China
*
Corresponding authors: Jie Chen; Email: chenjie@me.neu.edu.cn; Jizhuang Fan; Email: fanjizhuang@hit.edu.cn; Jie Zhao; Email: jzhao@hit.edu.cn
Corresponding authors: Jie Chen; Email: chenjie@me.neu.edu.cn; Jizhuang Fan; Email: fanjizhuang@hit.edu.cn; Jie Zhao; Email: jzhao@hit.edu.cn
Corresponding authors: Jie Chen; Email: chenjie@me.neu.edu.cn; Jizhuang Fan; Email: fanjizhuang@hit.edu.cn; Jie Zhao; Email: jzhao@hit.edu.cn
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Abstract

Accurate online estimation of the payload parameters benefits robot control. In the existing approaches, however, on the one hand, only the linear friction model was used for online payload identification, which reduced the online estimation accuracy. On the other hand, the estimation models contain much noise because of using actual joint trajectory signals. In this article, a new estimation algorithm based on parameter difference for the payload dynamics is proposed. This method uses a nonlinear friction model for the online payload estimation instead of the traditionally linear one. In addition, it considers the commanded joint trajectory signals as the computation input to reduce the model noise. The main contribution of this article is to derive a symbolic relationship between the parameter difference and the payload parameters and then apply it to the online payload estimation. The robot base parameters without payload were identified offline and regarded as the prior information. The one with payload can be solved online by the recursive least squares method. The dynamics of the payload can be then solved online based on the numerical difference of the two parameter sets. Finally, experimental comparisons and a manual guidance application experiment are shown. The results confirm that our algorithm can improve the online payload estimation accuracy (especially the payload mass) and the manual guidance comfort.

Keywords

online payload estimationparameter differencedynamic identificationnonlinear friction modelUR10 robot

Information

Type
Research Article
Information
Robotica , Volume 42 , Issue 8 , August 2024 , pp. 2690 - 2712
Copyright
© The Author(s), 2024. Published by Cambridge University Press

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References

Hirzinger, G., Sporer, N., Alin Albu-Schaffer, M. H., Krenn, R., Pascucci, A. and Schedl, M., “Dlr’s Torque-Controlled Light Weight Robot iii-are We Reaching the Technological Limits Now?,” In: 2002 IEEE International Conference on Robotics and Automation, (IEEE, 2022) pp. 1710–1716.CrossRefGoogle Scholar
Mavrakis, N. and Stolkin, R., “Estimation and exploitation of objects’ inertial parameters in robotic grasping and manipulation: A survey,” Robot Auton Syst 124, 103374 (2020).CrossRefGoogle Scholar
Marko Bjelonic, C. D. B., de Viragh, Y., Dhionis Sako, F. D. T., Jenelten, F. and Hutter, M., “Keep rollin’–whole-body motion control and planning for wheeled quadrupedal robots,” IEEE Robot Autom Lett 4(2), 2116–2123 (2019).CrossRefGoogle Scholar
Cherubini, A., Passama, R., Crosnier, A., Lasnier, A. and Fraisse, P., “Collaborative manufacturing with physical human–robot interaction,” Robot Comput Integr Manuf 40, 1–13 (2016).CrossRefGoogle Scholar
Duque, D. A., Prieto, F. A. and Hoyos, J. G., “Trajectory generation for robotic assembly operations using learning by demonstration,” Robot Comput Integr Manuf 57, 292–302 (2019).CrossRefGoogle Scholar
Canfield, S. L., Owens, J. S. and Zuccaro, S. G., “Zero moment control for lead-through teach programming and process monitoring of a collaborative welding robot,” J Mech Robot 13(3), 031016 (2021).CrossRefGoogle Scholar
Bai, J., Lian, S., Liu, Z., Wang, K. and Liu, D., “Deep learning based robot for automatically picking up garbage on the grass,” IEEE Trans Consum Electron 64(3), 382–389 (2018).CrossRefGoogle Scholar
Ding, C., Zhou, L., Li, Y. and Rong, X., “Locomotion control of quadruped robots with online center of mass adaptation and payload identification,” IEEE Access 8, 224578–224587 (2020).CrossRefGoogle Scholar
Abiko, S. and Yoshida, K.. On-Line Parameter Identification of a Payload Handled by Flexible Based Manipulator. In: 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (IEEE, 2004) pp. 2930–2935.CrossRefGoogle Scholar
Kubus, D., Kroger, T. and Wahl, F. M., “On-Line Estimation of Inertial Parameters Using a Recursive Total Least-Squares Approach," In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, (IEEE, 2008) pp. 3845–3852.CrossRefGoogle Scholar
Feng, D.-Z., Zhang, X.-D., Chang, D.-X. and Zheng, W. X., “A fast recursive total least squares algorithm for adaptive fir filtering,” IEEE Trans Signal Process 52(10), 2729–2737 (2004).CrossRefGoogle Scholar
Sánchez, M. C., Torres-Torriti, M. and Cheein, F. A., “Online inertial parameter estimation for robotic loaders,” IFAC-PapersOnLine 53(2), 8763–8770 (2020).CrossRefGoogle Scholar
Ma, Y., Wu, X., Wang, C., Yi, Z. and Liang, G., “Gait phase classification and assist torque prediction for a lower limb exoskeleton system using kernel recursive least-squares method,” Sensors 19(24), 5449 (2019).CrossRefGoogle ScholarPubMed
Tournois, G., Focchi, M., Prete, A. D., Orsolino, R., Caldwell, D. G. and Semini, C., “Online Payload Identification for Quadruped Robots,” In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (IEEE, 2017) pp. 4889–4896.CrossRefGoogle Scholar
Farsoni, S., Landi, C. T., Ferraguti, F., Secchi, C. and Bonfè, M., “Real-Time Identification of Robot Payload Using a Multirate Quaternion-Based Kalman Filter and Recursive Total Least-Squares,” In: 2018 IEEE International Conference on Robotics and Automation (ICRA), (IEEE, 2018) pp. 2103–2109.CrossRefGoogle Scholar
Farsoni, S., Landi, C. T., Ferraguti, F., Secchi, C. and Bonfe, M., “Compensation of load dynamics for admittance controlled interactive industrial robots using a quaternion-based kalman filter,” IEEE Robot Autom Lett 2(2), 672–679 (2017).CrossRefGoogle Scholar
Liu, J., Wang, P., Zha, F., Guo, W., Jiang, Z. and Sun, L., “A strong tracking mixed-degree cubature kalman filter method and its application in a quadruped robot,” Sensors 20(8), 2251 (2020).CrossRefGoogle Scholar
Armesto, L., Tornero, J. and Vincze, M., “Fast ego-motion estimation with multi-rate fusion of inertial and vision,” Int J Robot Res 26(6), 577–589 (2007).CrossRefGoogle Scholar
Farsoni, S., Ferraguti, F. and Bonfè, M., “Safety-oriented robot payload identification using collision-free path planning and decoupling motions,” Robot Comput Integr Manuf 59, 189–200 (2019).CrossRefGoogle Scholar
Khan, A. H., Li, S. and Luo, X., “Obstacle avoidance and tracking control of redundant robotic manipulator: An rnn-based metaheuristic approach,” IEEE Trans Ind Inform 16(7), 4670–4680 (2019).CrossRefGoogle Scholar
Renner, A., Wind, H. and Sawodny, O., “Online payload estimation for hydraulically actuated manipulators,” Mechatronics 66, 102322 (2020).CrossRefGoogle Scholar
Xu, T., Fan, J., Chen, Y., Ng, X., Ang, M. H., Fang, Q., Zhu, Y. and Zhao, J., “Dynamic identification of the kuka lbr iiwa robot with retrieval of physical parameters using global optimization,” IEEE Access 8, 108018–108031 (2020).CrossRefGoogle Scholar
Chang, X., An, H. and Ma, H., “Modeling and base parameters identification of legged robots,” Robotica 40(3), 747–761 (2022).CrossRefGoogle Scholar
Verdel, D., Bastide, S., Vignais, N., Bruneau, O. and Berret, B., “An identification-based method improving the transparency of a robotic upper limb exoskeleton,” Robotica 39(9), 1711–1728 (2021).CrossRefGoogle Scholar
Swevers, J., Ganseman, C., Tukel, D. B., de Schutter, J. and Van Brussel, H., “Optimal robot excitation and identification,” IEEE Trans Robot Autom 13(5), 730–740 (1997).CrossRefGoogle Scholar
Park, K.-J., “Fourier-based optimal excitation trajectories for the dynamic identification of robots,” Robotica 24(5), 625–633 (2006).CrossRefGoogle Scholar
Venture, G., Ayusawa, K. and Nakamura, Y., “A Numerical Method for Choosing Motions with Optimal Excitation Properties for Identification of Biped Dynamics-an Application to Human,” In: 2009 IEEE International Conference on Robotics and Automation, (IEEE, 2009) pp. 1226–1231.CrossRefGoogle Scholar
Bonnet, V., Fraisse, P., Crosnier, A.é, Gautier, M., González, A. and Venture, G., “Optimal exciting dance for identifying inertial parameters of an anthropomorphic structure,” IEEE Trans Robot 32(4), 823–836 (2016).CrossRefGoogle Scholar
Han, Y., Wu, J., Liu, C. and Xiong, Z., “An iterative approach for accurate dynamic model identification of industrial robots,” IEEE Trans Robot 36(5), 1577–1594 (2020).CrossRefGoogle Scholar
Scalera, L., Nainer, C., Giusti, A. and Gasparetto, A., “Robust safety zones for manipulators with uncertain dynamics in collaborative robotics,” Int J Comp Integ Manuf 1–13 (2023).Google Scholar
Swevers, J., Verdonck, W., Naumer, B., Pieters, S. and Biber, E., “An experimental robot load identification method for industrial application,” Int J Robot Res 21(8), 701–712 (2002).CrossRefGoogle Scholar
Swevers, J., Verdonck, W. and De Schutter, J., “Dynamic model identification for industrial robots,” IEEE Cont Syst Mag 27(5), 58–71 (2007).Google Scholar
Khalil, W., Gautier, M. and Lemoine, P., “Identification of the Payload Inertial Parameters of Industrial Manipulators,” In: Proceedings 2007 IEEE International Conference on Robotics and Automation, (IEEE, 2007) pp. 4943–4948.CrossRefGoogle Scholar
Hollerbach, J., Khalil, W. and Gautier, M., “Model Identification,” In: Springer Handbook of Robotics, (Springer, 2016) pp. 113–138.CrossRefGoogle Scholar
Salah, S., Sandoval, J., Ghiss, M., Laribi, M. A. and Zeghloul, S., “Online Payload Identification of a Franka Emika Robot for Medical Applications,” In: Proc Mechanisms and Machine Science, (Springer, 2020) pp. 130–136.CrossRefGoogle Scholar
Gaz, C. and De Luca, A., “Payload Estimation Based on Identified Coefficients of Robot Dynamics–with an Application to Collision Detection,” In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (IEEE, 2017) pp. 3033–3040.CrossRefGoogle Scholar
Rodriguez, A. S.-M., Ibanez, J. C. C. and Battle, V. F., “Online algebraic identification of the payload changes in a single-link flexible manipulator moving under gravity,” IFAC-PapersOnLine 47(3), 8397–8402 (2014).Google Scholar
Hu, J., Li, C., Chen, Z. and Yao, B., “Precision motion control of a 6-dofs industrial robot with accurate payload estimation,” IEEE-ASME Trans Mechatron 25(4), 1821–1829 (2020).CrossRefGoogle Scholar
Xu, T., Fan, J., Fang, Q., Zhu, Y. and Zhao, J., “An accurate identification method based on double weighting for inertial parameters of robot payloads,” Robotica 40(12), 4358–4374 (2022).CrossRefGoogle Scholar
Wu, J., Yu, G., Gao, Y. and Wang, L., “Mechatronics modeling and vibration analysis of a 2-DOF parallel manipulator in a 5-DOF hybrid machine tool,” Mech Mach Theory 121, 430–445 (2018).CrossRefGoogle Scholar
Wu, J., Zhang, B. B., Wang, L. P. and Yu, G., “An iterative learning method for realizing accurate dynamic feedforward control of an industrial hybrid robot,” Sci China Technol Sci 64(6), 1177–1188 (2021).CrossRefGoogle Scholar
Wu, J., Ye, H., Yu, G. and Huang, T., “A novel dynamic evaluation method and its application to a 4-DOF parallel manipulator,” Mech Mach Theory 168, 104627 (2022).CrossRefGoogle Scholar
Kurdas, A., Hamad, M., Vorndamme, J., Mansfeld, N., Abdolshah, S. and Haddadin, S., “Online Payload Identification for Tactile Robots using the Momentum Observer,” In: 2022 International Conference on Robotics and Automation (ICRA), (IEEE, 2022) pp. 5953–5959.CrossRefGoogle Scholar
Zhang, P. and Zhang, J., “Lower limb exoskeleton robots’ dynamics parameters identification based on improved beetle swarm optimization algorithm,” Robotica 40(8), 2716–2731 (2022).CrossRefGoogle Scholar
Farhat, N., Mata, V., Page, A. and Valero, F., “Identification of dynamic parameters of a 3-DOF RPS parallel manipulator,” Mech Mach Theory 43(1), 1–17 (2008).CrossRefGoogle Scholar
Gautier, M. and Khalil, W., “Direct calculation of minimum set of inertial parameters of serial robots,” IEEE Trans Robot Autom 6(3), 368–373 (1990).CrossRefGoogle Scholar
Khalil, W. and Bennis, F., “Comments on “direct calculation of minimum set of inertial parameters of serial robots,” IEEE Trans Robot Autom 10(1), 78–79 (1994).CrossRefGoogle Scholar
Ferguson, J. M., Ertop, T. E., Herrell, S. D. and Webster, R. J., “Unified robot and inertial sensor self-calibration,” Robotica 41(5), 1590–1616 (2023).CrossRefGoogle ScholarPubMed
Abedinifar, M., Ertugrul, S. and Arguz, S. H., “Nonlinear model identification and statistical verification using experimental data with a case study of the ur5 manipulator joint parameters,” Robotica 41(4), 1348–1370 (2023).CrossRefGoogle Scholar
Gaz, C., Magrini, E. and De Luca, A., “A model-based residual approach for human-robot collaboration during manual polishing operations,” Mechatronics 55, 234–247 (2018).CrossRefGoogle Scholar
Lindvig, A. P., Sdu robotics/ ur $\_$ rtde (2021). Accessed July 17, 2021. Available at: https://gitlab.com/sdurobotics/ur_rtde.Google Scholar
Xu, T., Fan, J., Fang, Q., Zhu, Y. and Zhao, J., “Robot dynamic calibration on current level: Modeling, identification and applications,” Nonlinear Dyn 109(4), 2595–2613 (2022).CrossRefGoogle Scholar
Gautier, M. and Briot, S., “Global identification of joint drive gains and dynamic parameters of robots,” J Dynam Syst Measure Cont 136(5), 051025 (2014).CrossRefGoogle Scholar
Li, Z., Wei, H., Liu, C., He, Y., Liu, G., Zhang, H. and Li, W., “An improved iterative approach with a comprehensive friction model for identifying dynamic parameters of collaborative robots,” Robotica 42(5), 1–23 (2024).CrossRefGoogle Scholar