Laser & Optoelectronics Progress, Volume. 61, Issue 14, 1428001(2024)

Visible Light Positioning System Based on SRU Neural Network in Coal Mine Underground

Gui Ru, Ling Qin*, Fengying Wang, Xiaoli Hu, and Yanhong Xu
Author Affiliations
  • School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, Inner Mongolia , China
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    This study proposes a visible light positioning system to enhance the accuracy of underground positioning in coal mines and simplify the positioning system based on a simple circulation unit (SRU). The system comprises a single LED light and four photodetectors, where the four photodetectors are positioned on the front, back, left, and right positions of a safety helmet, with the point to be measured located at the top center of the helmet. The SRU neural network predicts the position information of the measured point. Simulation results show that within the positioning area of 3.6 m × 3.6 m × 3 m, the proposed system achieves a positioning accuracy of 1.42 cm, an average positioning time of 0.59 s, and 97% point positioning errors within 2.3 cm. Compared with other positioning algorithms, the proposed system demonstrates substantially enhanced positioning accuracy. To further validate the system's performance, the entire positioning system is implemented in an actual environment. The experimental results reveal an average positioning error of 10.21 cm, which meets the requirements for underground positioning in coal mines.

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    Gui Ru, Ling Qin, Fengying Wang, Xiaoli Hu, Yanhong Xu. Visible Light Positioning System Based on SRU Neural Network in Coal Mine Underground[J]. Laser & Optoelectronics Progress, 2024, 61(14): 1428001

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    Paper Information

    Category: Remote Sensing and Sensors

    Received: Sep. 4, 2023

    Accepted: Nov. 20, 2023

    Published Online: Jul. 8, 2024

    The Author Email: Ling Qin (qinling1979@imust.edu.cn)

    DOI:10.3788/LOP232033

    CSTR:32186.14.LOP232033

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