Advertisement

AR-assisted robot welding programming

  • S. K. OngEmail author
  • A. Y. C. Nee
  • A. W. W. Yew
  • N. K. Thanigaivel
Article
  • 14 Downloads

Abstract

Robotic welding demands high accuracy and precision. However, robot programming is often a tedious and time-consuming process that requires expert knowledge. This paper presents an augmented reality assisted robot welding task programming (ARWP) system using a user-friendly augmented reality (AR) interface that simplifies and speeds up the programming of robotic welding tasks. The ARWP system makes the programming of robot welding tasks more user-friendly and reduces the need for trained programmers and expertise in specific robotic systems. The AR interface simplifies the definition of a welding path as well as the welding gun orientation, and the system; the system can locate the welding seam of a workpiece quickly and generate a viable welding path based on the user input. The developed system is integrated with the touch-sensing capability of welding robots in order to locate the welding path accurately based on the user input, for fillet welding. The system is applicable to other welding processes and methods of seam localization. The system implementation is described and evaluated with a case study.

Keywords

Augmented reality (AR) Robot programming Welding Seam tracking 

Notes

Acknowledgements

This research is supported by the Singapore A*STAR Agency for Science, Technology and Research, Science Engineering Research Council, Industrial Robotic Programme on Interface for Human Robot Interaction (Grant No. 1225100001) and the Public Sector Research Funding Programme on Embedding Powerful Computer Applications in Ubiquitous Augmented Reality Environments (Grant No. 1521200081).

References

  1. 1.
    Azuma RT (1997) A survey of augmented reality. Teleoperators Virtual Environ 6(4):355–385CrossRefGoogle Scholar
  2. 2.
    Wang X, Ong SK, Nee AYC (2016) A comprehensive survey of augmented reality assembly research. Adv Manuf 4(1):1–22CrossRefGoogle Scholar
  3. 3.
    Fang HC, Ong SK, Nee AYC (2014) Novel AR-based interface for human-robot interaction and visualization. Adv Manuf 2(4):275–288CrossRefGoogle Scholar
  4. 4.
    Lambrecht J, Kruger J (2012) Spatial programming for industrial robots based on gestures and augmented reality. In: Proceedings of IEEE/RSJ international conference on intelligent robots and systems, Washington DC, 7–12 Oct 2012, pp 466–472Google Scholar
  5. 5.
    Akan B, Ameri A, Çürüklü B et al (2011) Intuitive industrial robot programming through incremental multimodal language and augmented reality. In: Proceedings of 2011 IEEE international conference on robotics and automation, Shanghai, China, 9–13 May 2011, pp 3934–3939Google Scholar
  6. 6.
    Ni D, Yew AWW, Ong SK et al (2017) Haptic and visual augmented reality interface for programming welding robots. Adv Manuf 5(3):191–198CrossRefGoogle Scholar
  7. 7.
    Fang HC, Ong SK, Nee AYC (2017) Adaptive pass planning and optimization for robotic welding of complex joints. Adv Manuf 5(2):93–104CrossRefGoogle Scholar
  8. 8.
    Reinhart G, Munzert U, Vogl W (2008) A programming system for robot-based remote-laser-welding with conventional optics. CIRP Ann Manuf Technol 57(1):37–40CrossRefGoogle Scholar
  9. 9.
    Andersen RS, Bogh S, Moeslund TB et al (2015) Intuitive task programming of stud welding robots for ship construction. In: Proceedings of the 2015 IEEE international conference on industrial technology, Washington DC, 17–19 Mar 2015, pp 3302–3307Google Scholar
  10. 10.
    Zaeh MF, Vogl W (2006) Interactive laser-projection for programming industrial robots. In: Proceedings of IEEE/ACM international symposium on mixed and augmented reality, Washington DC, 22–25 Oct 2006, pp 125–128Google Scholar
  11. 11.
    Hiroi Y, Obata K, Suzuki K et al (2015) Remote welding robot manipulation using multi-view images. In: Proceedings of the 2015 IEEE international symposium on mixed and augmented reality, Washington DC, 29 Sept –3 Oct 2015, pp 128–131Google Scholar
  12. 12.
    Antonelli D, Astanin S, Galetto M et al (2013) Training by demonstration for welding robots by optical trajectory tracking. Procedia CIRP 12:145–150CrossRefGoogle Scholar
  13. 13.
    Yoshikawa T (1985) Manipulability of robotic mechanisms. Int J Robot Res 4(2):3–9MathSciNetCrossRefGoogle Scholar
  14. 14.
    Best PJ, McKay ND (1992) A method for registration of 3D shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239–256CrossRefGoogle Scholar
  15. 15.
    Garrido-Jurado S, Muñoz-Salinas R, Madrid-Cuevas FJ et al (2014) Automatic generation and detection of highly reliable fiducial markers under occlusion. Pattern Recogn 47(6):2280–2292CrossRefGoogle Scholar
  16. 16.
    Quigley M, Conley K, Gerkey BP et al (2009) ROS: an open-source robot operating system. In: Proceedings of 2009 IEEE international conference on robotics and automation, Kobe, Japan, 12–17 May, pp 5–10Google Scholar
  17. 17.
    Smits R (2017) KDL: kinematics and dynamics library. http://www.orocos.org/kdl. Accessed 12 Feb 2017
  18. 18.
    Şucan IA, Moll M, Kavraki LE (2012) The open motion planning library. IEEE Robot Autom Mag 19(4):72–82CrossRefGoogle Scholar

Copyright information

© Shanghai University and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Mechanical Engineering, Faculty of EngineeringNational University of SingaporeSingaporeSingapore

Personalised recommendations