Experimental platform for coal gangue sorting robot based on image detection

LI Sanxi; LI Ya'nan; WANG Zijie; HOU Peng; XUE Guanghui

doi:10.13272/j.issn.1671-251x.2022120028

Gong-kuang zidonghua (Jul 2023)

Experimental platform for coal gangue sorting robot based on image detection

LI Sanxi,
LI Ya'nan,
WANG Zijie,
HOU Peng,
XUE Guanghui

Affiliations

LI Sanxi
LI Ya'nan: School of Mechanical Electronic and Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
WANG Zijie: School of Mechanical Electronic and Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
HOU Peng: School of Mechanical Electronic and Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
XUE Guanghui: School of Mechanical Electronic and Information Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China

DOI: https://doi.org/10.13272/j.issn.1671-251x.2022120028
Journal volume & issue: Vol. 49, no. 7
pp. 107 – 113

Abstract

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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%.

Published in Gong-kuang zidonghua

ISSN: 1671-251X (Print)
Publisher: Editorial Department of Industry and Mine Automation
Country of publisher: China
LCC subjects: Technology: Mining engineering. Metallurgy
Website: http://www.gkzdh.cn/en.htm

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Abstract

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