Automation, Control and Intelligent Systems

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Visual Positioning and Grasping Application of Industrial Robot for Casting Parts

Received: 13 March 2019    Accepted:     Published: 23 May 2019
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Abstract

In order to guide an industrial robot to pick a rough casting object, a visual servoing system have been designed to perform the task. This solution present an efficient method to perform robot and vision system’s hand eye calibration. Shape matching method is used to find a rough casting object, by combining Line2d algorithm and ROI selection. This vision system can find rough objects with high computational speed and it is robust to environmental noise. The experiment shows that the system repeatability is within 2mm and verify the feasibility of the method and robustness of the algorithm.

DOI 10.11648/j.acis.20190701.13
Published in Automation, Control and Intelligent Systems (Volume 7, Issue 1, February 2019)
Page(s) 18-24
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Industrial Robot, Hand Eye Calibration, Template Matching, Machine Vision

References
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[2] Zhengke Qin, Peng Wang, Member, IEEE, Jia Sun, Jinyan Lu, and Hong Qiao, Senior Member, IEEE,“Precise Robotic Assembly for Large-Scale Objects Based on Automatic Guidance and Alignment”. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 65, NO. 6, JUNE. 2016.
[3] P. K. Allen, A. Timcenko, B. Yoshimi, and P. Michelman, “Automated tracking and grasping of a moving object with a robotic hand–eye system”, IEEE Trans. Robot. Autom., vol. 9, no. 2, pp. 152–165, Apr. 1993.
[4] Ke Xia, Zhengxin Weng,“Workpieces sorting system based on industrial robot of machine vision”, The 2016 3rd International Conference on Systems and Informatics (ICSAI 2016).
[5] Frank Cheng. “Robot Manipulation of 3D Cylindrical Objects with a Robot-Mounted 2D Vision Camera”, Computing Conference 2017 18-20 July 2017 | London, UK.
[6] Heiko Koch, Alexander König, Alexandra Weigl-Seitz, Karl Kleinmann, and Jozef Suchý, “Multisensor contour following with vision, force, and acceleration sensors for an industrial robot“, IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 62, NO. 2, FEBRUARY. 2013.
[7] Jae Byung Park, Seung Hun Lee and Il Jae Lee,“Precise 3D Lug Pose Detection Sensor for Automatic Robot Welding Using a Structured-Light Vision System”. Sensors 2009, 9,7550-7565doi:10.3390/s90907550.
[8] ZHANG ZY.“Camera calibration with one dimensional objects”. Computer Vision-European Conference on Computer Vision 2002. Berlin Heidelberg:Springer, 2002:161-174.
[9] ZHANG ZY. “A flexible new technique for camera calibration”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11):1330-1334.
[10] S. Hinterstoisser, Member, IEEE, Cedric Cagniart, Slobodan Ilic, Member, IEEE, Peter Sturm, Member, IEEE, Nassir Navab, Member, IEEE, Pascal Fua, Fellow, IEEE, and Vincent Lepetit,“Gradient Response Maps for Real-Time Detection of Textureless Objects”. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 34, NO. 5, MAY 2012.
[11] S. Hinterstoisser, V. Lepetit, S. Ilic, P. Fua, and N. Navab. “Dominant orientation templates for real-time detection of texture-less objects”. In CVPR, 2010.
[12] S. Hinterstoisser, S. Holzer, C. Cagniart, et al. “Multimodaltemplates for real-time detection of texture-less objects in heavily clutteredscenes”. International Conference on Computer Vision. IEEE Computer Society, 2011:858-865.
[13] Chan, Jacob, J. A. Lee, and Q. Kemao. "BIND: Binary Integrated Net Descriptors for Texture-Less Object Recognition." 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IEEE Computer Society, 2017.
Author Information
  • Department of Electronic Engineering School of Automation and In

  • Department of Information Engineering, Yangzhou University, Yang

  • Department of Electronic Engineering School of Automation and In

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  • APA Style

    Guoyang Wan, Fudong Li, Guofeng Wang. (2019). Visual Positioning and Grasping Application of Industrial Robot for Casting Parts. Automation, Control and Intelligent Systems, 7(1), 18-24. https://doi.org/10.11648/j.acis.20190701.13

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    ACS Style

    Guoyang Wan; Fudong Li; Guofeng Wang. Visual Positioning and Grasping Application of Industrial Robot for Casting Parts. Autom. Control Intell. Syst. 2019, 7(1), 18-24. doi: 10.11648/j.acis.20190701.13

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    AMA Style

    Guoyang Wan, Fudong Li, Guofeng Wang. Visual Positioning and Grasping Application of Industrial Robot for Casting Parts. Autom Control Intell Syst. 2019;7(1):18-24. doi: 10.11648/j.acis.20190701.13

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  • @article{10.11648/j.acis.20190701.13,
      author = {Guoyang Wan and Fudong Li and Guofeng Wang},
      title = {Visual Positioning and Grasping Application of Industrial Robot for Casting Parts},
      journal = {Automation, Control and Intelligent Systems},
      volume = {7},
      number = {1},
      pages = {18-24},
      doi = {10.11648/j.acis.20190701.13},
      url = {https://doi.org/10.11648/j.acis.20190701.13},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.acis.20190701.13},
      abstract = {In order to guide an industrial robot to pick a rough casting object, a visual servoing system have been designed to perform the task. This solution present an efficient method to perform robot and vision system’s hand eye calibration. Shape matching method is used to find a rough casting object, by combining Line2d algorithm and ROI selection. This vision system can find rough objects with high computational speed and it is robust to environmental noise. The experiment shows that the system repeatability is within 2mm and verify the feasibility of the method and robustness of the algorithm.},
     year = {2019}
    }
    

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    AU  - Guoyang Wan
    AU  - Fudong Li
    AU  - Guofeng Wang
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    AB  - In order to guide an industrial robot to pick a rough casting object, a visual servoing system have been designed to perform the task. This solution present an efficient method to perform robot and vision system’s hand eye calibration. Shape matching method is used to find a rough casting object, by combining Line2d algorithm and ROI selection. This vision system can find rough objects with high computational speed and it is robust to environmental noise. The experiment shows that the system repeatability is within 2mm and verify the feasibility of the method and robustness of the algorithm.
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    IS  - 1
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