Volume 49 Issue 7
Jul.  2023
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LI Sanxi, LI Ya'nan, WANG Zijie, et al. Experimental platform for coal gangue sorting robot based on image detection[J]. Journal of Mine Automation，2023，49(7)：107-113. DOI: 10.13272/j.issn.1671-251x.2022120028
Citation: LI Sanxi, LI Ya'nan, WANG Zijie, et al. Experimental platform for coal gangue sorting robot based on image detection[J]. Journal of Mine Automation，2023，49(7)：107-113. DOI: 10.13272/j.issn.1671-251x.2022120028
shu

Experimental platform for coal gangue sorting robot based on image detection

  • 1.

    Signal and Electronics Teaching and Research Office, Beijing Railway Electrification College, Beijing 102202, China

  • 2.

    School of Mechanical Electronic and Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China

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  • Received Date: December 07, 2022
  • Revised Date: July 24, 2023
  • Available Online: August 02, 2023
    Graphical Abstract
    • Abstract
  • Currently, coal gangue pre-sorting is still mostly done manually, with high labor intensity, low sorting efficiency, and safety hazards. Using coal gangue sorting robots to replace manual coal gangue pre-sorting is an effective way to ensure the health and safety of workers and improve work efficiency. However, the existing coal gangue sorting robots have poor performance in situations such as low light intensity and coal gangue surface covered with coal powder. To solve the above problems, an experimental platform for coal gangue sorting robot based on image detection is proposed. This experimental platform collects coal gangue images through industrial cameras. The platform uses ResNet18-YOLOv3 deep learning algorithm to identify the coal gangue in the images. The platform uses TCP communication to provide the position information of the gangue to the coal gangue sorting module for trajectory planning, then controls the manipulator to clamp the gangue and completes the gangue sorting operation. The platform uses the Halcon calibration method for hand-eye calibration of the experimental platform, in order to achieve the conversion of camera pixel coordinates and manipulator spatial coordinates. The positioning error of the experimental platform is calibrated. For coal gangue samples with sizes above 50 mm, the positioning error should not exceed 9 mm. The experimental results show that the recognition accuracy of the experimental platform for coal gangue under strong lighting conditions is 99%. The recognition accuracy of coal gangue under weak lighting conditions is 95%. The recognition accuracy of coal gangue under pulverized coal adhesion conditions is not less than 82%. The accuracy of coal gangue sorting is 82%.
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    • References (26)
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