VISUAL RECOGNITION SYSTEM OF WELDING ROBOT BASED ON MACHINE LEARNING, 9-16. SI

Yun Shi and Yanyan Zhu

References

  1. [1] W. Fei, L. Chen, H. Xiaoguang, R. Changlei, and L. Jinghong. Visual guidance of welding robots based on welding part recognition and posture estimation, Control and Decision- Making, 35(8), 2020, 1873–1878.
  2. [2] K. Abhishek, Reinforcement learning: Application and advances towards stable control strategies, Mechatronic Systems and Control, 51(1), 2023, 53–57.
  3. [3] Y. Li, C.L. Song, and X.Y. Chao, Modelling and application of a recti?er transformer with primary winding in series in a metallurgical rolling mill system, Mechatronic Systems and Control, 51(4), 2023, 128–192.
  4. [4] W. Xiangming, L. Mingchun, W. Haoren, and Z. Licheng, Automatic feeding robot visual recognition system, Journal of Shenyang University of Technology, 40(5), 2018, 564–570.
  5. [5] J. Baohua, Y. Changkui, Z. Weizheng, and Z. Weiwei, Review of research on fruit recognition in apple orchards based on machine vision, Journal of Light Industry, 34(2), 2019, 71–81.
  6. [6] T. Zhen, C. Guohua, G. Peng, and C. Qi, Identi?cation of intravenous drug dispensing robot medicine bottles based on machine vision and deep learning, Machine Tools and Hydraulics, 50(5), 2022, 33–37.
  7. [7] Z. Huimin, J. Liu, H. Chen, J. Chen, Y. Li, J. Xu, and W. Deng, Intelligent diagnosis using continuous wavelet transform and gauss convolutional deep belief network, IEEE Transactions on Reliability, 72(2), 2023, 692–702.
  8. [8] T. Souhir, Optimisation of network injected power of an innovated structure of wind turbine, Mechatronic Systems and Control, 51(2), 2023, 67–78.
  9. [9] L. Jing, C. Jinhai, P. Zhixuan, L. Jie, W. Wanneng, and Z. Guangbing, Robot grasping experimental system based on machine vision, Experimental Technology and Management, 39(4), 2022, 45–50.
  10. [10] Y. Hiroaki, T. Hasegawa, K. Nagahama, and M. Inaba, A research of construction method for autonomous tomato harvesting robot focusing on harvesting device and visual recognition, Journal of the Robotics Society of Japan, 36(10), 2018, 693–702.
  11. [11] Z. Dehong and D. Yan, Construction and software development of robot visual handling system, Packaging Engineering, 40(1), 2019, 149–155.
  12. [12] W. Guoyang, G. Wang, K. Xing, Y. Fan, and T. Yi, Robot visual measurement and grasping strategy for roughcastings, International Journal of Advanced Robotic Systems, 18(2), 2021, 715–720.
  13. [13] Z. Tianpeng and S. Longfei, Fault analysis of transmission line based on big data algorithm, Mechatronic Systems and Control, 50(4), 2022, 216–223.
  14. [14] Z. Ya, G. Jiahui, and L. Panchi, A median ?ltering scheme for quantum images, Journal of Electronics and Information, 43(1), 2021, 204–211.
  15. [15] X. Yuchao and L. Zhen, Research on the distribution of magnetic ?eld in reinforced concrete beams after damage based on the force-magnetic coupling model, Mechatronic Systems and Control, 50(3), 2022, 130–137.
  16. [16] Z. Liu, P. Wan, L. Ling, L. Chen, and W. Zhou, Recognition and grabbing system for workpieces exceeding the visual ?eld based on machine vision, Jiqiren/Robot, 40(3), 2018, 294–300 +308.
  17. [17] B.A. Gunes, B.A. Pearlmutter, A.A. Radul, and J.M. Siskind, Automatic di?erentiation in machine learning: A survey, Journal of Machine Learning Research, 18(153), 2018, 1–43.
  18. [18] W. Li, Automatic tracking algorithms based on wearable technology, Mechatronic Systems and Control, 50(1), 2022, 16–21.
  19. [19] A. Kumar, Reinforcement learning: Application and advances towards stable control strategies, 53-57. Si, Mechatronic Systems and Control, 51(1), 2023.
  20. [20] A. Kumar, J.J. Anand, and B.N. Hemanth Kumar, Intrusive video oculographic device: An eye-gaze-based device for communication, Innovation and Emerging Technologies, 9, 2022, 2250002.

Important Links:

Go Back