Volume 48 Issue 12
Dec.  2022
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YU Lu, TANG Chaoli, HUANG Yourui, et al. Research on tightly combined positioning method of coal mine robot based on UWB and IMU[J]. Journal of Mine Automation，2022，48(12)：79-85. DOI: 10.13272/j.issn.1671-251x.2022070058
Citation: YU Lu, TANG Chaoli, HUANG Yourui, et al. Research on tightly combined positioning method of coal mine robot based on UWB and IMU[J]. Journal of Mine Automation，2022，48(12)：79-85. DOI: 10.13272/j.issn.1671-251x.2022070058
shu

Research on tightly combined positioning method of coal mine robot based on UWB and IMU

  • School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China

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  • Received Date: July 20, 2022
  • Revised Date: December 02, 2022
  • Available Online: November 27, 2022
    Graphical Abstract
    • Abstract
  • The underground coal mine environment is complex. The existing coal mine robot positioning methods have low positioning precision and low real-time performance caused by the non-line-of-sight (NLOS) error and other factors. In order to solve the above problems, a tightly combined positioning method of coal mine robot based on UWB (Ultra Wide Band) and IMU (Inertial Measurement Unit) is proposed. Firstly, the UWB module is used to measure distance between the coal mine robot and UWB base station. The least square support vector machine (LSSVM) model is trained by using real value and measured value of the distance between the coal mine robot and the UWB base station, and the modified LSSVM model is obtained. Secondly, the measured value measured by the UWB module during the positioning process of the coal mine robot is used as the input of the modified LSSVM model. The modified LSSVM model is used to correct the measured value of UWB, reduce the influence of NLOS error on positioning precision, and obtain more accurate distance information. Finally, the range information modified by modified LSSVM model is used as the measurement input of error-state Kalman filter (ESKF). The measurement equation is formed with the position information solved by inertial navigation. The ESKF is used to tightly combine the UWB ranging correction value with the range information calculated by the inertial navigation to complete the state update. The more precise position information of the coal mine robot is obtained, and the precise positioning of the coal mine robot is achieved. The simulation results under different layont schemes of UWB base stations show that using modified LSSVM model can make the UWB range information more accurate, and improve the positioning precision. In the static positioning experiment, when the four UWB base stations are symmetrically distributed at the same height, the root mean square error of the positioning is reduced from 0.146 4 m to 0.139 8 m. When the four UWB base stations are distributed symmetrically with unequal heights, the root mean square error decreases from 0.300 8 m to 0.200 6 m. When the four base stations are distributed irregularly, the root mean square error decreases from 0.317 5 m to 0.314 2 m. Therefore, in actual scenarios, the UWB base stations should be arranged symmetrically at the same height as possible. In the dynamic positioning experiment, the fusion positioning trajectory corrected by the modified LSSVM model is closer to the real trajectory of the coal mine robot than the fusion positioning trajectory before correction. The result verifies that the tightly combined positioning method can reduce the NLOS error and improve positioning precision.
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