Login | Register

Online pose correction of an industrial robot using an optical coordinate measure machine system


Online pose correction of an industrial robot using an optical coordinate measure machine system

Gharaaty, Sepehr, Shu, Tingting, Joubair, Ahmed, Xie, Wen Fang and Bonev, Ilian A (2018) Online pose correction of an industrial robot using an optical coordinate measure machine system. International Journal of Advanced Robotic Systems, 15 (4). p. 172988141878791. ISSN 1729-8814

Text (application/pdf)
Shu-International Journal of Advanced Robotic Systems-2018.pdf - Published Version
Available under License Creative Commons Attribution.

Official URL: http://dx.doi.org/10.1177/1729881418787915


In this article, a dynamic pose correction scheme is proposed to enhance the pose accuracy of industrial robots. The dynamic pose correction scheme uses the dynamic pose measurements as feedback to accurately guide the robot end-effector to the desired pose. The pose is measured online with an optical coordinate measure machine, that is, C-Track 780 from Creaform. A root mean square method is proposed to filter the noise from the pose measurements. The dynamic pose correction scheme adopts proportional-integral-derivaitve controller and generates commands to the FANUC robot controller. The developed dynamic pose correction scheme has been tested on two industrial robots, FANUC LR Mate 200iC and FANUC M20iA. The experimental results on both robots demonstrate that the robots can reach the desired pose with an accuracy of ±0.050 mm for position and ±0.050° for orientation. As a result, the developed pose correction can make the industrial robots meet higher accuracy requirement in the applications such as riveting, drilling, and spot welding.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Mechanical, Industrial and Aerospace Engineering
Item Type:Article
Authors:Gharaaty, Sepehr and Shu, Tingting and Joubair, Ahmed and Xie, Wen Fang and Bonev, Ilian A
Journal or Publication:International Journal of Advanced Robotic Systems
  • Concordia Open Access Author Fund
  • Creaform
  • GE Aviation
  • Coriolis Composites
Digital Object Identifier (DOI):10.1177/1729881418787915
Keywords:Pose correction, visual servoing, pose accuracy, optical CMM, accuracy enhancement
ID Code:984107
Deposited On:07 Aug 2018 12:59
Last Modified:07 Aug 2018 12:59


1. Jiang Y, Jiang Y, Huang X, et al. An on-line compensation method of a metrology-integrated robot system for highprecision assembly. Ind Rob Int J 2016; 43(6): 647–656.

2. Alici G and Shirinzadeh B. A systematic technique to estimate positioning errors for robot accuracy improvement using laser interferometry based sensing. Mech Mach Theory 2005; 40: 879–906.

3. Greenway B. Robot accuracy. Ind Rob 2000; 27(4): 257–265.

4. Mooring BW, Roth ZS, and Driels MR. Fundamentals of manipulator calibration. Wiley-interscience, 1991.

5. Elatta A, Gen LP, Zhi FL, et al. An overview of robot calibration. Inf Technol J 2004; 3(1): 74–78.

6. Joubair A. Contribution a` lam´ elioration de la pr ´ ecision absolue des robots paralle`les. PhD Thesis, E´ cole de technologie sup´erieure, 2012.

7. Motta JMS, de Carvalho GC, and McMaster R. Robot calibration using a 3D vision-based measurement system with a single camera. Rob Comput Integr Manuf 2001; 17: 487–497.

8. Song Y, An G, and Zhang J. Positioning accuracy of a medical robotic system for spine surgery. In: 2nd international conference on biomedical engineering and informatics (BMEI’09) (eds P Qiu, C Yiu, H Zhang, and XWen), Tianjin, China, 17–19 October 2009, pp. 1–5, IEEE.

9. Joubair A, Nubiola A, and Bonev I. Calibration efficiency analysis based on five observability indices and two calibration models for a six-axis industrial robot. SAE Int J Aerosp 2013; 6(1): 161–168.

10. Nubiola A, Slamani M, Joubair A, et al. Comparison of two calibration methods for a small industrial robot based on an optical cmm and a laser tracker. Robotica 2014; 32(3): 447–466.

11. Gaudreault M, Joubair A, and Bonev IA. Local and closedloop calibration of an industrial serial robot using a new lowcost 3D measuring device. In: Robotics and automation (ICRA), IEEE international conference (ed A Okamura), Stanford University, Stockholm, Sweden, 16–21 May 2016, pp. 4312–4319. IEEE.

12. Joubair A and Bonev IA. Kinematic calibration of a six-axis serial robot using distance and sphere constraints. Int J Adv Manuf Technol 2015; 77(1–4): 515–523.

13. Hutchinson S, Hager GD, and Corke PI. A tutorial on visual servo control. IEEE Trans Robot Autom 1996; 12(5): 651–670.

14. Chaumette F and Hutchinson S. Visual servo control. I. Basic approaches. IEEE Robot Autom Mag 2006; 13(4): 82–90.

15. Bone GM and Capson D. Vision-guided fixtureless assembly of automotive components. Robot Comput Integr Manuf 2003; 19(1–2): 79–87.

16. Lertpiriyasuwat V and Berg MC. Adaptive real-time estimation of end-effector position and orientation using precise measurements of end-effector position. IEEE/ASME Trans Mechatron 2006; 11: 304–319.

17. Norman AR, Scho¨nberg A, Gorlach IA, et al. Validation of iGPS as an external measurement system for cooperative robot positioning. Int J Adv Manuf Technol 2013; 64(1–4): 427–446.

18. Jin Z, Yu C, Li J, et al. A robot assisted assembly system for small components in aircraft assembly. Ind Rob Int J 2014; 41: 413–420.

19. Posada JD, Schneider U, Pidan S, et al. High accurate robotic drilling with external sensor and compliance model-based compensation. In: Robotics and automation (ICRA), IEEE international conference (ed A Okamura), Stanford University, Stockholm, Sweden, 16–21 May 2016, pp. 3901–3907. IEEE.

20. Jos´e de Jesu´s R. Discrete time control based in neural networks for pendulums. Appl Soft Comput 2018; 68: 821–832.

21. Pan Y, Guo Z, Li X, et al. Output-feedback adaptive neural control of a compliant differential SMA actuator. IEEE Trans Control Syst Technol 2017; 25: 2202–2210.

22. Nikon. High accuracy robotics. http://www.nikonmetrology.com/en_US/Products/In-process-measurement/Adaptive-Robot-Control (2016, accessed 15 March 2016).

23. Hsiao T and Huang PH. Iterative learning control for trajectory tracking of robot manipulators. Int J Autom Smart Technol 2017; 7: 133–139.

24. Breth´e JF and Dakyo B. A stochastic ellipsoid approach to repeatability modelisation of industrial manipulator robots. In: Intelligent robots and systems, IEEE/RSJ international conference, EPLF Lausanne, Switzerland, 30 September–3 October 2002, Vol. 2, pp. 1608–1613. IEEE.

25. Gharaaty S. Accuracy enhancement of industrial robots by dynamic pose correction.Master’s Thesis, Concordia University, Canada, 2016.

26. Craig JJ. Introduction to robotics: mechanics and control, vol. 3. NJ, USA: Pearson/Prentice Hall Upper Saddle River, 2005.
All items in Spectrum are protected by copyright, with all rights reserved. The use of items is governed by Spectrum's terms of access.

Repository Staff Only: item control page

Downloads per month over past year

Back to top Back to top