AR-assisted robot welding programming

doi:10.1007/s40436-019-00283-0

Abstract

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.

The full text can be downloaded at https://link.springer.com/content/pdf/10.1007%2Fs40436-019-00283-0.pdf

Key words: Augmented reality (AR), Robot programming, Welding, Seam tracking

S. K. Ong, A. Y. C. Nee, A. W. W. Yew, N. K. Thanigaivel. AR-assisted robot welding programming[J]. Advances in Manufacturing, 2020, 8(1): 40-48.

TrendMD

References

1. Azuma RT (1997) A survey of augmented reality. Teleoperators Virtual Environ 6(4):355-385
2. Wang X, Ong SK, Nee AYC (2016) A comprehensive survey of augmented reality assembly research. Adv Manuf 4(1):1-22
3. Fang HC, Ong SK, Nee AYC (2014) Novel AR-based interface for human-robot interaction and visualization. Adv Manuf 2(4):275-288
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 466-472
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 3934-3939
6. Ni D, Yew AWW, Ong SK et al (2017) Haptic and visual augmented reality interface for programming welding robots. Adv Manuf 5(3):191-198
7. Fang HC, Ong SK, Nee AYC (2017) Adaptive pass planning and optimization for robotic welding of complex joints. Adv Manuf 5(2):93-104
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):37-40
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 3302-3307
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 125-128
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 128-131
12. Antonelli D, Astanin S, Galetto M et al (2013) Training by demonstration for welding robots by optical trajectory tracking. Procedia CIRP 12:145-150
13. Yoshikawa T (1985) Manipulability of robotic mechanisms. Int J Robot Res 4(2):3-9
14. Best PJ, McKay ND (1992) A method for registration of 3D shapes. IEEE Trans Pattern Anal Mach Intell 14(2):239-256
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):2280-2292
16. Quigley M, Conley K, Gerkey BP et al (2009) ROS:an opensource robot operating system. In:Proceedings of 2009 IEEE international conference on robotics and automation, Kobe, Japan, 12-17 May, pp 5-10
17. Smits R (2017) KDL:kinematics and dynamics library. http://www.orocos.org/kdl. Accessed 12 Feb 2017
18. Şucan IA, Moll M, Kavraki LE (2012) The open motion planning library. IEEE Robot Autom Mag 19(4):72-82

[1]	Yi-Wei Huang, Xiang-Dong Gao, Perry P. Gao, Bo Ma, Yan-Xi Zhang. Laser welding monitoring techniques based on optical diagnosis and artificial intelligence: a review [J]. Advances in Manufacturing, 2025, 13(2): 337-361.
[2]	Sheng Cai, Zhi-Chao Deng, Jia-Nan Wang, Nan Zhang. Dissimilar metals welding processes realized by vaporizing metal foils [J]. Advances in Manufacturing, 2025, 13(2): 430-443.
[3]	Clara Garcia, Mario Ortega, Eugenio Ivorra, Manuel Contero, Pau Mora, Mariano L. Alcañiz. Holorailway: an augmented reality system to support assembly operations in the railway industry [J]. Advances in Manufacturing, 2024, 12(4): 764-783.
[4]	Zhong-Jie Yue, Qiu-Ren Chen, Zu-Guo Bao, Li Huang, Guo-Bi Tan, Ze-Hong Hou, Mu-Shi Li, Shi-Yao Huang, Hai-Long Zhao, Jing-Yu Kong, Jia Wang, Qing Liu. Improving RSW nugget diameter prediction method: unleashing the power of multi-fidelity neural networks and transfer learning [J]. Advances in Manufacturing, 2024, 12(3): 409-427.
[5]	Blake Barnett, Anupam Vivek, Glenn Daehn. Shock effects on the upper limit of the collision weld process window [J]. Advances in Manufacturing, 2024, 12(2): 365-378.
[6]	Ji-Hong Dong, Yi-Ming Huang, Jia-Lei Zhu, Wei Guan, Xu-Kai Ren, Huan-Wei Yu, Lei Cui. Quality assessment of friction-stir-welded aluminum alloy welds via three-dimensional force signals [J]. Advances in Manufacturing, 2024, 12(1): 61-75.
[7]	Yong-Hua Shi, Zi-Shun Wang, Xi-Yin Chen, Yan-Xin Cui, Tao Xu, Jin-Yi Wang. Real-time K-TIG welding penetration prediction on embedded system using a segmentation-LSTM model [J]. Advances in Manufacturing, 2023, 11(3): 444-461.
[8]	Hao Su, Chuan-Song Wu. Numerical simulation for the comparison of thermal and plastic material flow behavior between symmetrical and asymmetrical boundary conditions during friction stir welding [J]. Advances in Manufacturing, 2023, 11(1): 143-157.
[9]	Y. K. Liu, S. K. Ong, A. Y. C. Nee. State-of-the-art survey on digital twin implementations [J]. Advances in Manufacturing, 2022, 10(1): 1-23.
[10]	C. Y. Siew, S. K. Ong, A. Y. C. Nee. Improving maintenance effi ciency and safety through a human-centric approach [J]. Advances in Manufacturing, 2021, 9(1): 104-114.
[11]	Yan-Xin Cui, Yong-Hua Shi, Qiang Ning, Yun-Ke Chen, Bao-Ri Zhang. Investigation into keyhole-weld pool dynamic behaviors based on HDR vision sensing of real-time K-TIG welding process through a steel/glass sandwich [J]. Advances in Manufacturing, 2021, 9(1): 136-144.
[12]	Ying-Zhong Tian, Hong-Fei Liu, Long Li, Wen-Bin Wang, Jie-Cai Feng, Feng-Feng Xi, Guang-Jie Yuan. Robust identification of weld seam based on region of interest operation [J]. Advances in Manufacturing, 2020, 8(4): 473-485.
[13]	M. Kimura, S. Iwamoto, M. Kusaka, K. Kaizu. Effect of core bar inserted into weld faying part to obtain an ideal pipe joint with non-generating inner flash via friction welding [J]. Advances in Manufacturing, 2020, 8(3): 418-428.
[14]	S. K. Ong, X. Wang, A. Y. C. Nee. 3D bare-hand interactions enabling ubiquitous interactions with smart objects [J]. Advances in Manufacturing, 2020, 8(2): 133-143.
[15]	M. Wasif Safeen, P. Russo Spena, G. Buffa, D. Campanella, A. Masnata, L. Fratini. Effect of position and force tool control in friction stir welding of dissimilar aluminum-steel lap joints for automotive applications [J]. Advances in Manufacturing, 2020, 8(1): 59-71.