Download pdf
A survey of offline precision calibration of industrial robots
- 1 Sichuan university
* Author to whom correspondence should be addressed.
Abstract
As the need for precision and efficiency grows in industrial manufacturing, the accuracy of industrial robots assumes paramount importance. Nevertheless, factors, such as mechanical structures and assembly processes often introduce errors, compromising robots' positioning and repeatability accuracy. Consequently, precise calibration and compensation of these errors are paramount for optimizing robots’ performance. A thorough analysis of error sources and the establishment of error models in industrial robots are conducted in the present study, with special attention paid to offline calibration methods. This paper reviews the robot error model building methods and offline accuracy calibration techniques, summarizes the technical difficulties of the calibration task, and proposes the future development direction for the difficulties such as the complexity of error modeling, the difficulty of non-motor calibration calculation, and the traditional stiffness compensation that does not meet the needs of the robot's operation process. The importance of this research lies in its potential to improve the accuracy and reliability of industrial robots, thus contributing to the development of industrial automation.
Keywords
industrial robots, offline accuracy calibration, error modeling, accuracy improvement, industrial automation.
[1]. Denavit J., Hartenberg R. (1955) A kinematic notation for lower-pair mechanisms based on matrices [J] Journalof Applied Mechanics. (22):215-221.
[2]. Hartenberg R., Denavit J. Kinematic synthesis of linkages [M]. New York:McGraw-Hill,1964.
[3]. Hayati S. Robot arm geometric link parameter estimation [C]. IEEE Conference on Decision and Control,1983.
[4]. Hayati S., Mirmirani M. Improving the absolute positioning accuracy of robot manipulators [J]. Journal ofRobotic Systems.1985,2(4):397-413.
[5]. Stone H., Sanderson A., Neuman C. Arm signature identification: Proceedings. 1986 IEEE International Conference on Robotics and Automation, 1986 [C]. IEEE.
[6]. Zhuang H., Roth Z., Hamano F. A complete and parametrically continuous kinematic model for robot manipulators [J]. Transactions on Robotics and Automation. 1992, 4(8).
[7]. Brockett R. Robotic manipulators and the product of exponentials formula [M]. Springer Berlin Heidelberg, 2006.
[8]. Wu Y., Li C., Li J., Li Z. Comparative Study of Robot Kinematic Calibration Algorithms using a Unified Geometric Framework [C]. Hong Kong: International Conference on Robotics and Automation, 2014.
[9]. Yang X., Wu L., Li J., et al. A minimal kinematic model for serial robot calibration using POE formula [J]. Robotics and Computer Integrated Manufacturing. 2014, 30(3): 326-334.
[10]. Dantam N. T. Robust and efficient forward, differential, and inverse kinematics using dual quaternions [J]. The International Journal of Robotics Research, 2021,40(10-11):1087-1105.
[11]. Judd R., Knasinski A. A technique to calibrate industrial robots with experimental verification [J]. Transactions on Robotics and Automation. 1990, 1(6): 20-30
[12]. Klimchik A., Pashkevich A., Chablat D. CAD-based approach for identification of elasto-static parameters of robotic manipulators [J]. Finite Elements in Analysis and Design, 2013, 75: 19-30.
[13]. Liu H. T., Huang T., Chetwynd D. G., Kecskemethy A. Stiffness Modeling of Parallel Mechanisms at Limb and Joint/Link Levels [J]. Ieee Transactions on Robotics, 2017, 33(3): 734-741.
[14]. Zhao C., Guo H. W., Zhang D., Liu R. Q., Li B., Deng Z. Q. Stiffness modeling of n (3RRlS) reconfigurable series-parallel manipulators by combining virtual joint method and matrix structural analysis [J]. Mechanism and Machine Theory, 2020, 152
[15]. Klimchik A., Pashkevich A., Caro S., Chablat D. Stiffness Matrix of Manipulators With Passive Joints: Computational Aspects [J]. Ieee Transactions on Robotics, 2012, 28(4): 955-958.
[16]. Detert T., Corves B. Extended Procedure for Stiffness Modeling Based on the Matrix Structure Analysis [C]. Joint International Conference of the 12th International Conference on Mechanisms and Mechanical Transmissions (MTM) / 23rd International Conference on Robotics (Robotics), 2016: 299-310.
[17]. C. Dumas, S. Caro, M. Cherif, S. Garnier et al., Joint stiffness identification of industrial serial robots, Robotica, vol. 30, pp. 649-659, July 2012.
[18]. T. Tsumugiwa, Y. Fukui and R. Yokogawa. Compliance measurement for the Mitsubishi PA-10 robot, Advanced Robotics, vol. 28, pp. 919-928, July 2014.
[19]. E. Abele, S. Rothenbücher and M. Weigold. Cartesian compliance model for industrial robots using virtual joints, Production Engineering, vol. 2, pp. 339-343, July 2008.
[20]. A. Klimchik, Y. Wu, S. Caro, B. Furet and A. Pashkevich. "Accuracy Improvement of Robot-Based Milling Using an Enhanced Manipulator Model", Mechanisms and Machine Science, vol. 22, pp. 73-81, 2014.
[21]. Lightcap C., Hamner S., Schmitz T., et al. Improved positioning accuracy of the PA10-6CE robot with geometric and flexibility calibration [J]. IEEE Transactions on Robotics, 2008, 24(2):452-456.
[22]. Marquardt D.W. An algorithm for least-squares esti-mation of nonlinear parameters [J]. Journal of the Society for Industrial and Applied Mathematics, 1963,11(2):431-441.
[23]. Mottajm S. T., DE Carvalho G. C., Mcmasterr S. Robot calibration using a 3D vision-based measurement system with a single camera [J]. Robotics and Computer-Integrated Manufacturing, 2001,17(6):487-497.
[24]. Ginanil S., Motta J. M. S. T. Theoretical and practicalaspects of robot calibration with experimental verification [J]. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2011, 33(1):15-21.
[25]. Omodei A., Legnani G., Adamini R. Three methodologies for the calibration of industrial manipulators. Experimental results on a SCARA robot [J]. Journal of Robotic Systems, 2000.17(6):291-307.
[26]. Renders J. M., Rossignol E., Becquet M., et al. Kinematic calibration and geometrical parameter identificationfor robots1l, IEEE Transactions on Robotics and Automation. 1991.7(6):721-732.
[27]. Fan C. Zhao G., et al. Calibration of a parallel mechanism in a serial-parallel polishing machine tool based on genetic algorithm1. International Journal of Advanced Manufacturing Echnology, 2015.81(1):27-37.
[28]. Jang J.H., Kim S. H, Kwak Y. K. Calibration of geometric and non-geometric errors of an industrial robot [J] Robotica, 2001,19(3):311-321.
[29]. Zhong X.L., Lewis J., Nancy F. L. Inverse robot calibration using artificial neural networks [J]. Engineering Applications of Artificial Intelligence, 1996, 9(1): 83-93.
[30]. Gao G., Sun G., Na J., et al. Structural parameter identification for 6 DOF industrial robots[J]. Mechanical Systems and Signal Processing. 2018, 113: 145-155.
[31]. Messay T., Ordóñez R., Marcil E. Computationally efficient and robust kinematic calibration methodologies and their application to industrial robots [J]. Robotics and Computer-Integrated Manufacturing. 2016, 37: 33-48.
[32]. Mustafa S., Tao P., Yang G., et al. A geometrical approach for online error compensation of industrial manipulators [C]. IEEE International Conference on Advanced Intelligent Mechatronics, 2010.
[33]. Russell D., Todd S. Improved Accuracy of Unguided Articulated Robots [J]. Sae International Journal of Aerospace. 2009, 1(2): 40-45.
[34]. Zeng Y.F., Liao W. H, Tian W. Multi-objective optimization of samples for industrial robot error compensation [J]. Robot, 2017, 39(2):239-248.
[35]. Hong P., Tian W., Mei D. Q., et al. Robotic variable parameter accuracy compensation using space grid. Robot, 2015, 37(3):327-335.
[36]. Nubiola A., Bonev I. Absolute calibration of an ABB IRB 1600 robot using a laser tracker [J]. Robotics and Computer Integrated Manufacturing. 2013, 29(1): 236-245.
[37]. Zhu Z. R., Chen C., Peng F.Y., Duan X.Y., Wei D. Q. Identification of joint position-dependent stiffness parameters and analysis of robot milling deformation[J]International Journal of Advanced Manufacturing Technology, 2022,118(11-12)4179-4193.
[38]. Filion A., Joubair A., Tahan A. S., et al. Robot calibration using a portable photogrammetry system [J]. Robotics and Computer-Integrated Manufacturing. 2018, 49: 77-87.
[39]. Jiao J. C., Tian W., Zhang L., et al. Variable Stiffiness Identification and Configuration Optimization of Industrial Robots for Machining Tasks [J]. Chinese Journal of Mechanical Engineering, 2022, 35(1).
[40]. Cen L., Melkote S N. CCT-based mode coupling chatter avoidance in robotic milling [J]. Journal of Manufacturing Processes, 2017, 29: 50-61.
[41]. Yang K., Yang W., Cheng G., Lu B. A new methodology for joint stiffness identification of heavy duty industrial robots with the counterbalancing system [J]. Robotics and Computer-Integrated Manufacturing, 2018, 53: 58-71.
[42]. Lin J., Li Y., Xie Y., Hu J., Min J. Joint stiffness identification of industrial serial robots using 3D digital image correlation techniques [J]. Proceedings of the Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science, 2022, 236(1): 536-551.
[43]. Slavkovic N. R., Milutinovic D. S., Glavonjic M M . A method for off-line compensation of cutting force-induced errors in robotic machining by tool path modification [J]. The International Journal of Advanced Manufacturing Technology, 2014, 70(9-12):2083-2096.
[44]. Gong C. H., Yuan J. X., Ni J. Nongeometric error identification and compensation for robotic system by inverse calibration [J]. International Journal of Machine Tools & Manufacture, 2000, 40(14): 2119-2137.
[45]. Zeng Y., Tian W., Li D., et al. An error-similarity-based robot positional accuracy improvement method for a robotic drilling and riveting system [J]. The International Journal of Advanced Manufacturing Technology, 2017, 88(9/10/11/12):2745-2755.
[46]. Cai, Y., Yuan, P., Shi, Z. et al. Application of Universal Kriging for Calibrating Offline-Programming Industrial Robots. [J] Intell Robot Syst 94, 339-348 (2019).
[47]. N. Takanashi. 6 DOF manipulators absolute positioning accuracy improvement using a neural-network, EEE International Workshop on Intelligent Robots and Systems, Towards a New Frontier of Applications, Ibaraki, Japan, 1990, pp. 635-640 vol.2.
[48]. Dali Wang and Ying Bai. Improving Position Accuracy of Robot Manipulators Using Neural Networks, 2005 IEEE Instrumentationand Measurement Technology Conference Proceedings, Ottawa, ON, Canada, 2005, pp. 1524-1526.
[49]. Wang D. L., Bai Y., Zhang J. Y. Robot manipulator calibration using neural network and a camera-based measurement-ment system [J]. Transactions of the Institute of Measurement-ment and Control, 2012, 34(1): 105-121.
Cite this article
Yang,X. (2024). A survey of offline precision calibration of industrial robots. Applied and Computational Engineering,81,16-24.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).