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Fuzzy-PI double-layer stability control of an online vision-based tracking system

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Abstract

In many manufacturing processes (e.g., welding, spraying, coating adhesive), the control for the velocity in the main direction heavily affects operation quality. In addition to traditional manual operations, the industrial robot with a tracking system is capable of accurate and stable velocity control. In this paper, an intelligent robot tracking system is designed for implementing an appropriate velocity control and improving the performance of an autonomous system with online structured light vision tracking. For this aim, an effective tracking algorithm is proposed based on position-based visual servoing (PBVS), and motion compensation is implemented according to both detected path and taught path. To improve the adaptability of the system, a Fuzzy-PI double-layer controller is developed, which adjusts the movement of the end effector in both cases of large and small deviation. Welding experiments demonstrate the effectiveness of the proposed vision tracking system.

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References

  1. Rout A, Deepak BBVL, Biswal BB (2019) Advances in weld seam tracking techniques for robotic welding: a review. Robot Comput Integr Manuf 56:12–37

    Article  Google Scholar 

  2. Zhang L, Ye Q, Yang W, Jiao J (2014) Weld line detection and tracking via spatial-temporal cascaded hidden Markov models and cross structured light. IEEE Trans Instrum Meas 63(4):742–753

    Article  Google Scholar 

  3. Caggiano A, Nele L, Sarno E, Teti R (2014) 3D digital reconfiguration of an automated welding system for a railway manufacturing application, Vol. 25 of Procedia CIRP, pp 39–45

  4. Rodríguez-Martín M, Rodríguez-Gonzálvez P, González-Aguilera D, Fernández-Hernández J (2017) Feasibility study of a structured light system applied to welding inspection based on articulated coordinate measure machine data. IEEE Sens J 17(13):4217–4224

    Article  Google Scholar 

  5. Xu, Lv N, Han Y, Chen S (2016) IEEE, research on the key technology of vision sensor in robotic welding. In: IEEE workshop on advanced robotics and its social impacts, pp 121–125

  6. Shao WJ, Huang Y, Zhang Y (2018) A novel weld seam detection method for space weld seam of narrow butt joint in laser welding. Optics Laser Technol 99:39–51

    Article  Google Scholar 

  7. Xue B, Chang B, Peng G, Gao Y, Tian Z, Du D, Wang G (2019) A vision based detection method for narrow butt joints and a robotic seam tracking system. Sensors 19(5):1144

    Article  Google Scholar 

  8. Guo J, Zhu Z, Sun B, Yu Y (2019) A novel multifunctional visual sensor based on combined laser structured lights and its anti-jamming detection algorithms. Weld World 63(2):313–322

    Article  Google Scholar 

  9. Chaumette F, Hutchinson S (2006) Visual servo control—part I: Basic approaches. IEEE Robot Autom Mag 13(4):82–90

    Article  Google Scholar 

  10. Fang Z, Xu D, Tan M (2010) Visual seam tracking system for butt weld of thin plate. Int J Adv Manuf Technol 49(5–8):519–526

    Article  Google Scholar 

  11. Kiddee P, Fang Z, Tan M (2016) An automated weld seam tracking system for thick plate using cross mark structured light. Int J Adv Manuf Technol 87(9–12):3589–3603

    Article  Google Scholar 

  12. Babazadeh Tili R, Akbarnejad F, Rostami V (2018) Visual torch position control using fuzzy-servoing controller for arc welding process. J Comput Robot 11(1):57–67

    Google Scholar 

  13. Xu Y, Fang G, Chen S, Zou JJ, Ye Z (2014) Real-time image processing for vision-based weld seam tracking in robotic gmaw. Int J Adv Manuf Technol 73(9):1413–1425

    Article  Google Scholar 

  14. Kos M, Arko E, Kosler H, Jezeršek M (2019) Remote laser welding with in-line adaptive 3d seam tracking. Int J Adv Manuf Technol 103(9):4577–4586. https://doi.org/10.1007/s00170-019-03875-z

    Article  Google Scholar 

  15. Rios-Cabrera R, Morales-Diaz AB, Aviles-Viñas JF, Lopez-Juarez I (2016) Robotic gmaw online learning: issues and experiments. Int J Adv Manuf Technol 87(5):2113–2134. https://doi.org/10.1007/s00170-016-8618-0

    Article  Google Scholar 

  16. Righetti L, Kalakrishnan M, Pastor P, Binney J, Kelly J, Voorhies RC, Sukhatme GS, Schaal S (2014) An autonomous manipulation system based on force control and optimization. Auton Robots 36(1):11–30

    Article  Google Scholar 

  17. Gu WP, Xiong ZY, Wan W (2013) Autonomous seam acquisition and tracking system for multi-pass welding based on vision sensor. Int J Adv Manuf Technol 69(1):451–460

    Article  Google Scholar 

  18. Zhou L, Lin T, Chen SB (2006) Autonomous acquisition of seam coordinates for arc welding robot based on visual servoing. J Intell Robot Syst 47(3):239–255

    Article  Google Scholar 

  19. Zhang Z, Wen G, Chen S (2019) On-line monitoring and defects detection of robotic arc welding: a review and future challenges. Springer, Berlin, pp 3–28

    Google Scholar 

  20. Huang W, Kovacevic R (2012) Development of a real-time laser-based machine vision system to monitor and control welding processes. Int J Adv Manuf Technol 63(1–4):235–248

    Article  Google Scholar 

  21. Carron A, Arcari E, Wermelinger M, Hewing L, Hutter M, Zeilinger MN (2019) Data-driven model predictive control for trajectory tracking with a robotic arm. IEEE Robot Autom Lett 4(4):3758–3765

    Article  Google Scholar 

  22. Liangyu L, Lingjian F, Xin Z, Xiang L (2006) Image processing of seam tracking system using laser vision. In: International conference on robotic welding, intelligence and automation, RWIA 2006, December 8–December 11, 2006, vol 362 of Lecture Notes in Control and Information Sciences, Springer, pp 319–324

  23. Sung K, Lee H, Choi YS, Rhee S (2009) Development of a multiline laser vision sensor for joint tracking in welding. Weld J 88(4):79S–85S

    Google Scholar 

  24. Chen XZ, Chen SB, Lin T (2007) Recognition of macroscopic seam for complex robotic welding environment, vol 362 of Lecture notes in control and information sciences, pp 171–+

  25. Dinham M, Fang G (2014) Detection of fillet weld joints using an adaptive line growing algorithm for robotic arc welding. Robot Comput Integr Manuf 30(3):229–243

    Article  Google Scholar 

  26. Wang N, Zhong K, Shi X, Zhang X (2020) A robust weld seam recognition method under heavy noise based on structured-light vision. Robot Comput Integr Manuf 61:101821

    Article  Google Scholar 

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Acknowledgements

The authors would like to gratefully acknowledge the reviewers comments. This work was supported by National Key R&D Program of China (Grant Nos.2019YFB1310200), National Natural Science Foundation of China (Grant Nos.U1713207) and Science and Technology Program of Guangzhou (Grant Nos.201904020020).

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Authors and Affiliations

  1. South China University of Technology Guangzhou, Guangzhou, China

    Nianfeng Wang, Kaifan Zhong, Xiaodong Shi & Xianmin Zhang

  2. Shenzhen Polytechnic, Shenzhen, P. R. China

    Liang Zhang

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  1. Nianfeng Wang
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  2. Kaifan Zhong
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  3. Xiaodong Shi
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  4. Xianmin Zhang
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  5. Liang Zhang
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Correspondence to Nianfeng Wang.

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Wang, N., Zhong, K., Shi, X. et al. Fuzzy-PI double-layer stability control of an online vision-based tracking system. Intel Serv Robotics 14, 187–197 (2021). https://doi.org/10.1007/s11370-021-00356-9

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  • DOI: https://doi.org/10.1007/s11370-021-00356-9

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