Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-24T02:24:00.588Z Has data issue: false hasContentIssue false
  • English
  • Français

A Parameter Dimension Reduction-Based Estimation Approach to Enhance the Kinematic Accuracy of a Parallel Hardware-in-the-Loop Docking Simulator

Published online by Cambridge University Press:  20 October 2020

Yan Hu Open the ORCID record for Yan Hu [Opens in a new window] ,
Feng Gao ,
Xianchao Zhao ,
Tianhao Yang ,
Haoran Shen ,
Chenkun Qi  and
Rui Cao
Yan Hu
Affiliation:
State Key Lab of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China. E-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
Feng Gao*
Affiliation:
State Key Lab of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China. E-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
Xianchao Zhao
Affiliation:
State Key Lab of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China. E-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
Tianhao Yang
Affiliation:
Shanghai Aerospace Equipment Manufacturer Co., Ltd., Shanghai 200245, PR China E-mails: [email protected], [email protected]
Haoran Shen
Affiliation:
Shanghai Aerospace Equipment Manufacturer Co., Ltd., Shanghai 200245, PR China E-mails: [email protected], [email protected]
Chenkun Qi
Affiliation:
State Key Lab of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China. E-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
Rui Cao
Affiliation:
State Key Lab of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China. E-mails: [email protected], [email protected], [email protected], [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

The docking simulators are significant ground test equipment for aerospace projects. The fidelity of docking simulation highly depends on the accuracy performance. This paper investigates the kinematic accuracy for the developed docking simulator. A novel kinematic calibration method which can reduce the number of parameters for error modeling is presented. The principle of parameters separation is studied. A simplified error model is derived based on Taylor series. This method can contribute to the simplification of the error model, fewer measurements, and easier convergence during the parameters identification. The calibration experiment validates this method for further accuracy enhancement.

Keywords

KinematicsAccuracyParallel robotHardware-in-the-loopDocking simulator
Type
Article
Information
Robotica , Volume 39 , Issue 6 , June 2021 , pp. 959 - 974
Copyright
© The Author(s), 2020. 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.)

References

Yaskevich, A., “Real time math simulation of contact interaction during spacecraft docking and berthing,” J. Mech. Eng. Auto. 4, 1–15 (2014).Google Scholar
Uyama, N., Fujii, Y., Nagaoka, K. and Yoshida, K., “Experimental Evaluation of Contact-Impact Dynamics Between a Space Robot with a Compliant Wrist and a Free-Flying Object,” In: International Symposium on Artificial Intelligence, Robotics and Automation in Space, Turin, Italy (2012).Google Scholar
Qi, C. K., Zhao, X. C., Gao, F., Ren, A. Y. and Hu, Y., “Low-order model based divergence compensation for hardware-in-the-loop space discrete contact,” J. Intell. Robot. Syst. 86(1), 81–93 (2017).CrossRefGoogle Scholar
Qi, C. K., Zhao, X. C., Gao, F., Ren, A. Y. and Hu, Y., “Divergence compensation for hardware-in-the-loop simulation of stiffness-varying discrete contact in space,” Acta Astronaut. 128, 295–303 (2016).CrossRefGoogle Scholar
Daney, D., “Kinematic calibration of the Gough Platform,” Robotica 21(6), 677–690 (2003).CrossRefGoogle Scholar
Gong, C. H., Yuan, J. X and Ni, J., “Nongeometric error identification and compensation for robotic system by inverse calibration,” Int. J. Mach. Tools Manuf. 40(14), 2119–2137 (2000).CrossRefGoogle Scholar
Zhang, H., Cheng, G., Shan, X. and Guo, F., “Kinematic accuracy research of 2(3HUS S) parallel manipulator for simulation of hip joint motion,” Robotica 36(9), 1386–1401 (2018).CrossRefGoogle Scholar
Renders, J. M., Rossignol, E., Becquet, M. and Hanus, R., “Kinematic calibration and geometrical parameter identification for robots,” IEEE Trans. Robot. Automat. 7(6), 721–732 (1992).CrossRefGoogle Scholar
Chen, J. G. and Chao, L. M., “Positioning error analysis for robot manipulators with all rotary joints,” Proceedings of International Conference on Robotics and Automation, vol. 3(6) (1986) pp. 1011–1016.Google Scholar
Becquet, M., “Analysis of flexibility sources in robot structures,” Proceedings of IMACS/IFAC International Symposium Modelling and Simulation Distributed Parameter System (1987) pp. 419–424.Google Scholar
Lurascu, C. C. and Park, F. C., “Geometric algorithms for kinematic calibration of robots containing closed loops,” J. Mech. Des. 125(1), 23–32 (2003).Google Scholar
Huang, P., Wang, J. S., Wang, L. P. and Yao, R., “Kinematic calibration of a hybrid machine tool with regularization method,” Int. J. Mach. Tools Manuf. 51(3), 210–220 (2010).CrossRefGoogle Scholar
Mooring, B. W., Fundamentals of Manipulator Calibration (J. Wiley, New York, 1991).Google Scholar
Merlet, J. P., Parallel Robots (Springer, New York, 2006).Google Scholar
Khalil, W., Gautier, M. and Enguehard, C., “Identifiable parameters and optimum configurations for robots calibration,” Robot. 9(1), 63–70 (1991).CrossRefGoogle Scholar
Zhuang, H. Q., “Self-calibration of parallel mechanisms with a case study on Stewart platforms,” IEEE Trans. Robot. Automat. 13(3), 387–397 (1997).CrossRefGoogle Scholar
Patel, A. J. and Ehmann, K. F., “Calibration of a hexapod machine tool using a redundant leg,” Int. J. Mach. Tools Manuf. 40(4), 489–512 (2000).CrossRefGoogle Scholar
Takeda, Y., Shen, G. and Funabashi, H., “A DBB-based kinematic calibration method for in-parallel actuated mechanisms using a Fourier series,” J. Mech. Des. 126(5), 856–865 (2004).CrossRefGoogle Scholar
Huang, T., Chetwynd, D. G., Whithouse, D. J. and Wang, J. S., “A general and novel approach for parameter identification of 6-DOF parallel kinematic machines,” Mech. Mach. Theory 40(2), 219–239 (2005).CrossRefGoogle Scholar
Renaud, P., Andreff, N., Martinet, P. and Gogu, G., “Kinematic calibration of parallel mechanisms: A novel approach using legs observation,” IEEE Trans. Robot. 21(4), 529–538 (2005).CrossRefGoogle Scholar
Ren, X., Feng, Z. and Su, C., “A new calibration method for parallel kinematics machine tools using orientation constraint,” Int. J. Mach. Tools Manuf. 49(9), 708–721 (2009).CrossRefGoogle Scholar
He, R., Zhao, Y., Yang, S. and Yang, S., “Kinematic-parameter identification for serial-robot calibration based on POE formula,” IEEE Trans. Robot. 26(3), 411–423 (2010).Google Scholar
Legnani, G. and Tiboni, M., “Optimal design and application of a low-cost wire-sensor system for the kinematic calibration of industrial manipulators,” Mech. Mach. Theory 73(2), 25–48 (2014).CrossRefGoogle Scholar
Yu, D. Y., Li, H. R. and Chen, W.F., “Kinematic calibration of parallel robots for docking mechanism motion simulation,” Int. J. Adv. Robot. Syst. 8(4), 1079–1084 (2011).CrossRefGoogle Scholar
Zhang, X.G., Zou, H. W. and Chen, W. D., “The research of kinematic calibration method of parallel 6-DOF docking platform,” J. Astronaut. 28(2), 273–277 (2007).Google Scholar
Yu, D. Y., “Kinematic calibration of super-length journey parallel robots for spacecraft docking motion simulation platform,” Recent Pat. Mech. Eng. 10 (2017), 296–303.Google Scholar
Yu, D. Y., Cong, D. C. and Han, J. W., “Accuracy analysis of motion simulator of docking mechanism,” J. Harbin Inst. Technol. 40(2), 264–268 (2008).Google Scholar
Dai, X., Huang, Q., Han, J. and Li, H., “Accuracy synthesis of Stewart platform used in testing system for spacecraft docking mechanism,” Int. Conf. Meas. Technol. Mechatron. Automat. 3, 7–10 (2009).Google Scholar
Yu, D. Y., “Pose Accuracy Compensation of Parallel Robots Using RBF Neural Network,” In: Control Decision Conference (2008) pp. 1857–1861. doi: 10.1109/CCDC.2008.4597645 CrossRefGoogle Scholar
Huang, Q. T., Jiang, H. Z., Zhang, S. Y. and Han, J. W., “Spacecraft docking simulation using hardware-in-the-loop simulator with Stewart platform” Chin. J. Mech. Eng. 18(3) (2005).CrossRefGoogle Scholar
Mitchell, J. D., Cryan, S. P., Baker, K. and Chien, C.H., “Integrated docking simulation and testing with the Johnson space center six-degree-of-freedom dynamic test system,” Proceedings of Space Technology and Applications International Forum (2008) pp. 709–716.Google Scholar
Grigore, G., Mobility of mechanisms: A critical review. Mech. Mach. Theory 40(9), 1068–1097 (2005).Google Scholar
Chablat, D. and Wenger, P., Architecture optimization of a 3-DOF translational parallel mechanism for machining applications, the Orthoglide. IEEE Trans. Robot. Automat. 19(3), 403–410 (2003).CrossRefGoogle Scholar