Volume 48 Issue 7
Aug.  2022
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SONG Danyang, YANG Jinheng, TAO Xinya, et al. Shearer positioning method based on non-holonomic constraints[J]. Journal of Mine Automation，2022，48(7)：52-57. DOI: 10.13272/j.issn.1671-251x.2022020006
Citation: SONG Danyang, YANG Jinheng, TAO Xinya, et al. Shearer positioning method based on non-holonomic constraints[J]. Journal of Mine Automation，2022，48(7)：52-57. DOI: 10.13272/j.issn.1671-251x.2022020006
shu

Shearer positioning method based on non-holonomic constraints

  • 1.

    National and Provincial Joint Engineering Laboratory of Mining Intelligent Electrical Apparatus Technology, Taiyuan University of Technology, Taiyuan 030024, China

  • 2.

    School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China

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  • Received Date: February 06, 2022
  • Revised Date: July 14, 2022
  • Available Online: March 14, 2022
    Graphical Abstract
    • Abstract
  • At present, the shearer positioning method is based on the combination of the inertial navigation system and odometer. The method directly uses the output of the odometer to correct the shearer forward speed calculated by the inertial navigation system. However, the capability of suppressing the error divergence of the inertial navigation system is very limited. The shearer in the process of movement meets the characteristics of the non-holonomic constraints. When the shearer does not jump and sideslip, the lateral velocity and vertical velocity at the connection between the traction gear and the crawler are zero. Based on this characteristic, a new shearer positioning method based on non-holonomic constraints is proposed on the basis of the combination of the inertial navigation system and odometer. The output of the inertial measurement unit arranged in the middle of the shearer's body is mechanically arranged, so as to obtain the attitude, speed and position information of the shearer. The output of the odometer installed on the traction gear of the shearer is used to calculate the instantaneous velocity of the shearer. The Kalman filtering state equation is established by using a mechanical arrangement result of the inertial navigation system and an error propagation model. The non-integrity constraint is introduced at the joint of a traction gear and a crawler of the shearer. The Kalman filtering observation equation is established by using the difference between the velocity projected at the joint by the inertial navigation system and the velocity output by the mileometer as an observation vector. The output of the inertial navigation system is modified by using the results of the Kalman filtering algorithm as error feedback. Then the optimal estimation of the attitude, speed and position of the shearer is obtained. The experimental results show that compared with the traditional combined positioning method of inertial navigation system and odometer, the positioning error does not diverge with time after the non-holonomic constraint is added. The positioning method has good tracking performance on the actual trajectory. The positioning errors of the shearer in the forward, lateral and vertical directions are reduced by 66%, 62% and 67% respectively.
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    • References (12)
  • [1]
    王世博,葛世荣,王世佳,等. 长壁综采工作面无人自主开采发展路径与挑战[J]. 煤炭科学技术,2022,50(2):231-243. DOI: 10.13199/j.cnki.cst.2020-1150

    WANG Shibo,GE Shirong,WANG Shijia,et al. Development and chanllege of unmanned autonomous longwall fully-mechanized coal mining face[J]. Coal Science and Technology,2022,50(2):231-243. DOI: 10.13199/j.cnki.cst.2020-1150
    [2]
    周开平. 薄煤层综采工作面采煤机组合定位方法研究[J]. 工矿自动化,2019,45(6):52-57,68. DOI: 10.13272/j.issn.1671-251x.2019010096

    ZHOU Kaiping. Research on combined positioning method of shearer on fully mechanized mining face of thin coal seam[J]. Industry and Mine Automation,2019,45(6):52-57,68. DOI: 10.13272/j.issn.1671-251x.2019010096
    [3]
    张守祥,李森,宋来亮. 基于惯性导航和里程仪的煤矿采掘设备定位[J]. 工矿自动化,2018,44(5):52-57. DOI: 10.13272/j.issn.1671-251x.2018010042

    ZHANG Shouxiang,LI Sen,SONG Lailiang. Positioning of coal mining equipments based on inertial navigation and odometer[J]. Industry and Mine Automation,2018,44(5):52-57. DOI: 10.13272/j.issn.1671-251x.2018010042
    [4]
    夏婷. 综采工作面刮板输送机直线度检测方法研究[D]. 徐州: 中国矿业大学, 2019.

    XIA Ting. Study on the measurement method of scraper conveyor straightness in fully mechanized mining face[D]. Xuzhou: China University of Mining and Technology, 2019.
    [5]
    许晓伟,赖际舟,吕品,等. 基于采煤机工作面端头量测的改进因子图高精度自主定位方法研究[J]. 控制与决策,2022,37(8):2170-2176.

    XU Xiaowei,LAI Jizhou,LYU Pin,et al. High-precision autonomous positioning method based on improved factor graph of measurements at both ends of shearer working face[J]. Control and Decision,2022,37(8):2170-2176.
    [6]
    YANG Hai,LUO Tao,LI Wei,et al. A stable SINS/UWB integrated positioning method of shearer based on the multi-model intelligent switching algorithm[J]. IEEE Access,2019,7:29128-29138. DOI: 10.1109/ACCESS.2019.2898212
    [7]
    万文辉,李宇,胡文敏,等. 基于联邦滤波进行立体相机/IMU/里程计运动平台组合导航定位[J]. 武汉大学学报(信息科学版),2018,43(1):101-106.

    WAN Wenhui,LI Yu,HU Wenmin,et al. Mobile platform localization by integration of stereo cameras,IMU and wheel qdometer based on federated filter[J]. Geomatics and Information Science of Wuhan University,2018,43(1):101-106.
    [8]
    鲁程,王世博,葛世荣,等. 多惯导冗余的采煤机定位原理及其合理性分析[J]. 煤炭学报,2019,44(增刊2):746-753. DOI: 10.13225/j.cnki.jccs.2019.0491

    LU Cheng,WANG Shibo,GE Shirong,et al. Redundant multi-INS positioning algorithm of shearer and analysis of its rationality[J]. Journal of China Coal Society,2019,44(S2):746-753. DOI: 10.13225/j.cnki.jccs.2019.0491
    [9]
    WANG Shijia. Research on shearer positioning with double-INS[J]. Sensor Review,2019,39(4):577-584. DOI: 10.1108/SR-12-2018-0318
    [10]
    秦永元, 张洪钺, 汪叔华. 卡尔曼滤波与组合导航原理[M]. 3版. 西安: 西北工业大学出版社, 2015.

    QIN Yongyuan, ZHANG Hongyue, WANG Shuhua. Kalman filtering and integrated navigation principle[M]. 3rd ed. Xi'an: Northwestern Polytechnical University Press, 2015.
    [11]
    郑江涛,李四海,刘士明,等. 基于惯导和激光雷达的采煤机定位方法[J]. 中国惯性技术学报,2020,28(5):595-602. DOI: 10.13695/j.cnki.12-1222/o3.2020.05.005

    ZHENG Jiangtao,LI Sihai,LIU Shiming,et al. Positioning method of a shearer based on inertial navigation and lidar[J]. Journal of Chinese Inertial Technology,2020,28(5):595-602. DOI: 10.13695/j.cnki.12-1222/o3.2020.05.005
    [12]
    沈阳,王鹏江,吉晓冬,等. 二维里程辅助的掘进机自主导航方法研究[J]. 仪器仪表学报,2021,42(11):96-105. DOI: 10.19650/j.cnki.cjsi.J2107815

    SHEN Yang,WANG Pengjiang,JI Xiaodong,et al. Research on autonomous navigation method of roadheader aided by two-dimensional mileage[J]. Chinese Journal of Scientific Instrument,2021,42(11):96-105. DOI: 10.19650/j.cnki.cjsi.J2107815
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