Error compensation of collaborative robot dynamics based on deep recurrent neural network

XU Zheng; ZHANG Gong; WANG Huo-ming; HOU Zhi-cheng; YANG Wen-lin; LIANG Ji-min; WANG Jian; GU Xing

doi:10.13374/j.issn2095-9389.2020.04.30.003

Volume 43 Issue 7

Jul. 2021

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Article Navigation > Chinese Journal of Engineering > 2021 > 43(7): 995-1002

XU Zheng, ZHANG Gong, WANG Huo-ming, HOU Zhi-cheng, YANG Wen-lin, LIANG Ji-min, WANG Jian, GU Xing. Error compensation of collaborative robot dynamics based on deep recurrent neural network[J]. Chinese Journal of Engineering, 2021, 43(7): 995-1002. doi: 10.13374/j.issn2095-9389.2020.04.30.003

Citation:

XU Zheng, ZHANG Gong, WANG Huo-ming, HOU Zhi-cheng, YANG Wen-lin, LIANG Ji-min, WANG Jian, GU Xing. Error compensation of collaborative robot dynamics based on deep recurrent neural network[J]. Chinese Journal of Engineering, 2021, 43(7): 995-1002. doi: 10.13374/j.issn2095-9389.2020.04.30.003

Citation:

XU Zheng, ZHANG Gong, WANG Huo-ming, HOU Zhi-cheng, YANG Wen-lin, LIANG Ji-min, WANG Jian, GU Xing. Error compensation of collaborative robot dynamics based on deep recurrent neural network[J]. Chinese Journal of Engineering, 2021, 43(7): 995-1002. doi: 10.13374/j.issn2095-9389.2020.04.30.003

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Error compensation of collaborative robot dynamics based on deep recurrent neural network

doi: 10.13374/j.issn2095-9389.2020.04.30.003

1.
Intelligent Robot & Equipment Center, Guangzhou Institute of Advanced Technology, Chinese Academy of Science, Guangzhou 511458, China
2.
School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China

More Information

Corresponding author: E-mail: gong.zhang@giat.ac.cn
Received Date: 2020-04-30
Available Online: 2020-07-21
Publish Date: 2021-07-01

Abstract

Abstract

Establishing the dynamics model of robot and its parameters is significant for simulation analysis, control algorithm verification, and implementation of human–machine interaction. Especially under various working conditions, the errors of the calculated predicted torque of each axis have the most direct negative effect. The general robot dynamics model rarely takes the minor and complex characteristics into consideration, such as the reducer flexibility, inertia force of motor rotors, and friction. However, as the structure of collaborative robots is lighter and smaller than the ordinary industrial robots, the characteristics neglected by general dynamics models account for a relatively large amount. The above facts result in a large error in the calculation and prediction of collaborative robots analysis. To address the short comings of general robot dynamics model, a network based on long short-term memory (LSTM) in deep recurrent neural network was proposed. The network compensates the general dynamics model of a self-developed six-degree-of-freedom collaborative robot based on the consideration of gravity, Coriolis force, inertial force, and friction force. In the experiment, the nondisassembly experimental measurement combined with least-squares method was used to identify the parameters. The motor current was used to evaluate the joint torque instead of mounting an expensive and inconvenient torque sensor. The excitation trajectory based on the Fourier series was optimized. The raw experimental data were used to train the proposed LSTM network. About the accuracy of the dynamic model and the compensation method for the collaborative robot, the root-mean-square error of the calculated torque relative to the actual measured torque was used to train the network and evaluate the proposed method. The analysis and the results of the experiment show that the compensated collaborative robot dynamics model based on LSTM network displays a good prediction on the actual torque, and the root-mean-square error between predicted and actual torques is reduced from 61.8% to 78.9% compared to the traditional model, the effectiveness of the proposed error compensation policy is verified.
- collaborative robot,
- dynamics model,
- error compensation,
- recurrent neural network,
- long short-term memory

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References(25)

References

[1]	An C H, Atkeson C G, Hollerbach J. Model-Based Control of a Robot Manipulator. Cambridge: MIT Press, 1988
[2]	Xiang W W K, Yan S Z. Dynamic analysis of space robot manipulator considering clearance joint and parameter uncertainty: Modeling, analysis and quantification. Acta Astron, 2020, 169: 158 doi: 10.1016/j.actaastro.2020.01.011
[3]	Norouzzadeh S, Lorenz T, Hirche S. Towards safe physical human-robot interaction: An online optimal control scheme // The 21st IEEE International Symposium on Robot and Human Interactive Communication. Paris, 2012: 503
[4]	Craig J J. Introduction to Robotics: Mechanics and Control. 4th Ed. Pearson, 2017
[5]	Siciliano B, Sciavicco L, Villani L, et al. Robotics: Modelling, Planning and Control. London: Springer, 2015
[6]	陶岳, 趙飛, 曹巨江. 協作機器人關節摩擦特性辨識與補償技術. 組合機床與自動化加工技術, 2019(4):28 Tao Y, Zhao F, Cao J J. Research on friction characteristics identification and compensation of cooperative robot's joints. Modular Mach Tool Autom Manuf Tech, 2019(4): 28
[7]	Simoni L, Beschi M, Legnani G, et al. On the inclusion of temperature in the friction model of industrial robots. IFAC-PapersOnLine, 2017, 50(1): 3482 doi: 10.1016/j.ifacol.2017.08.933
[8]	Ossadnik D, Guadarrama-Olvera J R, Dean-Leon E, et al. Adaptive friction compensation for humanoid robots without joint-torque sensors // IEEE-RAS International Conference on Humanoid Robots (Humanoids). Beijing, 2018: 980
[9]	張鐵, 洪景東, 李秋奮, 等. 基于BP神經網絡的機器人波動摩擦力矩修正方法. 工程科學學報, 2019, 41(8):1085 Zhang T, Hong J D, Li Q F, et al. Wave friction correction method for a robot based on BP neural network. Chin J Eng, 2019, 41(8): 1085
[10]	Vitiello V, Tornambe A. Adaptive compensation of modeled friction using a RBF neural network approximation // 46th IEEE Conference on Decision and Control. New Orleans, 2007: 4699
[11]	Qiu L K, Zhao Y Z, Zhang Y X. Adaptive friction identification and compensation based on RBF neural network for the linear inverted pendulum // Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology. Harbin, 2011: 385
[12]	Yu W, Heredia J A. PD control of robot with RBF networks compensation // Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium. Como, 2000: 329
[13]	孫昌國, 馬香峰, 譚吉林. 機器人操作器慣性參數的計算. 機器人, 1990, 12(2):19 Sun C G, Ma X F, Tan J L. Calculation of inertia parameter of robot manipulator. Robot, 1990, 12(2): 19
[14]	Armstrong B, Khatib O, Burdick J. The explicit dynamic model and inertial parameters of the PUMA 560 arm // Proceedings of 1986 IEEE International Conference on Robotics and Automation. San Francisco, 1986: 510
[15]	Olsen M, Petersen H G. A new method for estimating parameters of a dynamic robot model. IEEE Trans Rob Autom, 2001, 17(1): 95 doi: 10.1109/70.917088
[16]	Khalil W, Guegan S. Inverse and direct dynamic modeling of Gough-Stewart robots. IEEE Trans Rob, 2004, 20(4): 754 doi: 10.1109/TRO.2004.829473
[17]	Swevers J, Naumer B, Pieters S, et al. An experimental robot load identification method for industrial application // Experimental Robotics VIII. Springer, Berlin, Heidelberg, 2003: 318
[18]	丁亞東, 陳柏, 吳洪濤, 等. 一種工業機器人動力學參數的辨識方法. 華南理工大學學報(自然科學版), 2015, 43(3):49 Ding Y D, Chen B, Wu H T, et al. An identification method of industrial robot’s dynamic parameters. J South China Univ Technol Nat Sci Ed, 2015, 43(3): 49
[19]	Khalil W, Restrepo P. An efficient algorithm for the calculation of the filtered dynamic model of robots // Proceedings of IEEE International Conference on Robotics and Automation. Minneapolis, 1996: 323
[20]	劉正士, 陳恩偉, 干方建. 機器人慣性參數辨識的若干方法及進展. 合肥工業大學學報(自然科學版), 2005, 28(9):998 Liu Z S, Chen E W, Gan F J. Review of some methods for identifying inertial parameters of robots and their development. J Hefei Univ Technol Nat Sci, 2005, 28(9): 998
[21]	Yoshida K, Khalil W. Verification of the positive definiteness of the inertial matrix of manipulators using base inertial parameters. Int J Rob Res, 2000, 19(5): 498 doi: 10.1177/02783640022066996
[22]	Deb K, Jain H. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, Part I: solving problems with box constraints. IEEE Trans Evol Comput, 2014, 18(4): 577 doi: 10.1109/TEVC.2013.2281535
[23]	Graves A. Long Short-Term Memory. Berlin: Springer, 2012: 1735
[24]	Jozefowicz R, Zaremba W, Sutskever I. An empirical exploration of recurrent network architectures // Proceedings of the 32nd International Conference on International Conference on Machine Learning. Lile, France, 2015: 2342
[25]	Kingma D, Ba J. Adam: A method for stochastic optimization //International Conference for Learning Representations. San Diego, 2015: 1