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IJAT Vol.16 No.2 pp. 208-217
doi: 10.20965/ijat.2022.p0208
(2022)

Paper:

Views over last 60 days: 690

Pose Estimation of a Small Connector Attached to the Tip of a Cable Sticking Out of a Circuit Board

Changjian Ying*,†, Yaqiang Mo*, Yuichiro Matsuura**, and Kimitoshi Yamazaki***

*Graduate School of Science and Technology, Shinshu University
4-17-1 Wakasato, Nagano City, Nagano 380-8553, Japan

†Corresponding author

**Seiko Epson Corporation, Suwa, Japan

***Faculty of Engineering Mechanical Systems Engineering, Shinshu University, Nagano, Japan

Received:
May 14, 2021
Accepted:
September 13, 2021
Published:
March 5, 2022
Creative Commons License
Keywords:
pose estimation, point cloud registration, deep learning
Abstract

In this study, we present the pose estimation of a small connector attached to the tip of a cable sticking out from a circuit board. Since such connectors are generally small and float in various configurations on their workpieces, it is difficult to achieve automation of grasping and inserting the connector into the corresponding socket. We focus on the task of grasping a connector and propose methods for detecting the location of the connector and estimating its 6DoF pose. In this regard, we use a high-precision three-dimensional digitizer to capture the object point cloud and combine several methods, such as deep learning and registration, to perform data processing. We conducted grasp experiments on several connectors using an actual industrial robot and confirmed the effectiveness of our approach in terms of pose estimation.

Cite this article as:
C. Ying, Y. Mo, Y. Matsuura, and K. Yamazaki, “Pose Estimation of a Small Connector Attached to the Tip of a Cable Sticking Out of a Circuit Board,” Int. J. Automation Technol., Vol.16 No.2, pp. 208-217, 2022.
Data files:
References
  1. [1] K. Sumi, “Development of production robot system that can handle flexible goods “project for strategic development of advanced robot element technologies / Robot Assembly System for FA equipment”,” IEEE Workshop on Advanced Robotics and its Social Impacts, pp. 42-46, doi: 10.1109/ARSO.2009.5587079, 2009.
  2. [2] J. Huang, P. Di, T. Fukuda, and T. Matsuno, “Fault-tolelant Mating Process of Electric Connectors in Robotic Wiring Harness Assembly Systems,” Proc. of the 7th World Congress on Intelligent Control and Automation, pp. 2339-2344, doi: 10.1109/WCICA.2008.4593288, 2008.
  3. [3] K. Sano, S. Iijima, and K. Yamazaki, “A Case Study on Automated Manipulation for Hooking Wiring of Flexible Flat Cables,” 2019 IEEE Int. Conf. on Mechatronics and Automation, pp. 793-798, doi: 10.1109/ICMA.2019.8816286, 2019.
  4. [4] C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space,” 31st Conf. on Neural Information Processing Systems (NIPS 2017), 2017.
  5. [5] F. Chen, F. Cannella, J. Huang, H. Sasaki, and T. Fukuda, “A Study on Error Recovery Search Strategies of Electronic Connector Mating for Robotic Fault-Tolerant Assembly,” J. of Intelligent and Robotic Systems, Vol.81, No.2, pp. 257-271, doi: 10.1007/s10846-015-0248-5, 2016,
  6. [6] F. Yumbla, J.-S. Yi, M. Abayebas, M. Shafiyev, and H. Moon, “Tolerance dataset: mating process of plug-in cable connectors for wire harness assembly tasks,” Intelligent Service Robotics, Vol.13, No.1, pp. 159-168, doi: 10.1007/s11370-019-00307-5, 2020.
  7. [7] H.-C. Song, Y.-L. Kim, D.-H. Lee, and J.-B. Song, “Electric connector assembly based on vision and impedance control using cable connector-feeding system,” J. of Mechanical Science and Technology, Vol.31, No.12, pp. 5997-6003, doi: 10.1007/s12206-017-1144-7, 2017.
  8. [8] D. Romeres, D. K. Jha, W. Yerazunis, D. Nikovski, and H. A. Dau, “Anomaly Detection for Insertion Tasks in Robotic Assembly Using Gaussian Process Models,” 2019 18th European Control Conf. (ECC), pp. 1017-1022, doi: 10.23919/ECC.2019.8795698, 2019.
  9. [9] X. Jiang, K.-M. Koo, K. Kikuchi, A. Konno, and M. Uchiyama, “Robotized assembly of a wire harness in car production line,” 2010 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 490-495, doi: 10.1109/IROS.2010.5653133, 2010.
  10. [10] F. Yumbla, M. Abeyabas, T. Luong, J.-S. Yi, and H. Moon, “Preliminary Connector Recognition System Based on Image Processing for Wire Harness Assembly Tasks,” 2020 20th Int. Conf. on Control, Automation and Systems (ICCAS), pp. 1146-1150, doi: 10.23919/ICCAS50221.2020.9268291, 2020.
  11. [11] Y. Kimura, H. Takauji, S. Kaneko, Y. Domae, and H. Okuda, “Shape recognition of flexible cables by Outer Edge FCM clustering,” 2011 17th Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV), pp. 1-5, doi: 10.1109/FCV.2011.5739735, 2011.
  12. [12] Y. Domae, H. Okuda, Y. Kitaaki, Y. Kimura, H. Takauji, K. Sumi, and S. Kaneko, “3-D Sensing for Flexible Linear Object Alignment in Robot Cell Production System,” J. Robot. Mechatron., Vol.22, No.1, pp. 100-111, doi: 10.20965/jrm.2010.p0100, 2010.
  13. [13] Y. Kitaaki, R. Haraguchi, K. Shiratsuchi, Y. Domae, H. Okuda, A. Noda, K. Sumi, T. Fukuda, S. Kaneko, and T. Matsuno, “A robotic assembly system capable of handling flexible cables with connector,” 2011 IEEE Int. Conf. on Mechatronics and Automation, pp. 893-897, doi: 10.1109/ICMA.2011.5985708, 2011.
  14. [14] R. B. Rusu and S. Cousins, “3D is here: Point Cloud Library (PCL),” 2011 IEEE Int. Conf. on Robotics and Automation, pp. 1-4, doi: 10.1109/ICRA.2011.5980567, 2011.
  15. [15] W. Wohlkinger and M. Vincze, “Ensemble of shape functions for 3D object classification,” 2011 IEEE Int. Conf. on Robotics and Biomimetics, pp. 2987-2992, doi: 10.1109/ROBIO.2011.6181760, 2011.
  16. [16] Y. Guo, H. Wang, Q. Hu, H. Liu, L. Liu, and M. Bennamoun, “Deep Learning for 3D Point Clouds: A Survey,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.43, No.12, pp. 4338-4384, doi: 10.1109/tpami.2020.3005434, 2020.
  17. [17] C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation,” 2017 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 77-85, doi: 10.1109/CVPR.2017.16, 2017.
  18. [18] Q.-Y. Zhou, J. Park, and V. Koltun, “Open3D: A Modern Library for 3D Data Processing,” arXiv, abs/1801.09847, 2018.
  19. [19] A. M. Bronstein, M. M. Bronstein, and R. Kimmel (Eds.), “Numerical Geometry of Non-Rigid Shapes,” Springer, doi: 10.1007/978-0-387-73301-2, 2009.
  20. [20] P. J. Besl and N. D. Mckay, “A Method for Registration of 3-D shapes,” IEEE Trans. Pattern Anal. Mach. Intell., Vol.14, pp. 239-256, doi: 10.1109/34.121791, 1992.
  21. [21] B. Drost and S. Ilic, “3d object detection and localization using multimodal point pair features,” 2012 2nd Int. Conf. on 3D Imaging, Modeling, Processing, Visualization & Transmission, pp. 9-16, doi: 10.1109/3DIMPVT.2012.53, 2012.
  22. [22] R. B. Rusu, N. Blodow, and M. Beetz, “Fast Point Feature Histograms (FPFH) for 3D registration,” 2009 IEEE Int. Conf. on Robotics and Automation, pp. 3212-3217, doi: 10.1109/ROBOT.2009.5152473, 2009.
  23. [23] N. Mellado, D. Aiger, and N. Mitra, “Super 4PCS Fast Global Pointcloud Registration via Smart Indexing,” Computer Graphics Forum, Vol.33, No.5, pp. 205-215, doi: 10.1111/cgf.12446, 2014.
  24. [24] “PhoXi 3D Scanner XS.” https://www.photoneo.com/products/phoxi-scan-xs/ [Accessed May 14, 2021]
  25. [25] https://www.epson.jp/products/robots/6axis/n2/ [Accessed May 14, 2021]

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Last updated on Nov. 22, 2024