Volume 48 Issue 3
Mar.  2022
Turn off MathJax
Article Contents
Article Navigation >  Journal of Mine Automation  > 2022  >  48(3): 78-85
ZHOU Yibo, ZHOU Weiyi, GUO Zhenwu, et al. Multi-sensor fusion positioning of detection robot for tailings pond flood discharge tunnel[J]. Journal of Mine Automation，2022，48(3)：78-85.  doi: 10.13272/j.issn.1671-251x.2021090063
Citation: ZHOU Yibo, ZHOU Weiyi, GUO Zhenwu, et al. Multi-sensor fusion positioning of detection robot for tailings pond flood discharge tunnel[J]. Journal of Mine Automation，2022，48(3)：78-85.  doi: 10.13272/j.issn.1671-251x.2021090063
shu

Multi-sensor fusion positioning of detection robot for tailings pond flood discharge tunnel

doi: 10.13272/j.issn.1671-251x.2021090063
  • 1.

    College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China

  • 2.

    State Grid Yueqing Power Supply Company, Wenzhou 311799, China

  • Received Date: 2021-09-17
  • Accepted Date: 2022-03-02
  • Rev Recd Date: 2022-01-26
    Abstract
  • The environment in the flood discharge tunnel of tailings pond is complex, and the existing positioning algorithm of detection robot has the problem of positioning failure in weak texture indoor scene, which is difficult to be applied to the precise positioning and accumulative error elimination of detection robot in this kind of environment. In order to solve the above problems, a multi-sensor fusion positioning algorithm of detection robot for tailings flood discharge tunnel is proposed. Based on the odometry method and graph theory, the algorithm simplifies the long-distance positioning in the flood discharge tunnel environment to multi-segment short-distance positioning by using the ArUco code. Firstly, the odometer is calibrated by UMBmark algorithm, which effectively eliminates two types of system errors of wheel diameter and axle diameter. Secondly, the extended Kalman filter (EKF) algorithm is used to fuse the information of the odometer and the inertial measurement unit (IMU), and the odometry method is used to realize the calculation of the position and attitude information of the detection robot during the movement process. Finally, the ArUco code is used as a road sign and fixed inside the tunnel. The detection robot carries the calibrated camera to identify and process the ArUco code information. The robot uses the measurement values of each sensor to form constraints, and combines constraints with the graph optimization method to achieve position and attitude optimization. And according to the information carried by the ArUco code, the accumulative error is eliminated so as to realize the long-distance high-precision positioning of the detection robot in the narrow and long space and weak texture scene. The multi-sensor fusion positioning algorithm is operated and closed in the actual scene of the tailings pond flood discharge tunnel, and 10 groups of repeated positioning experiments traveling 40 m are carried out respectively. The results show that the multi-sensor fusion positioning algorithm has high stability and precision, can correct the accumulative error effectively and realize the precise positioning of the detection robot in the flood discharge tunnel environment. The average positioning error of traveling 20 m is 19.77 cm, the average positioning error of traveling 40 m is 21.23 cm, and the average corrected error of traveling 20 m is 4.2 mm.

     

    • FullText(HTML)
  • loading
    • References(17)
  • [1]
    张彪. 尾矿库在线监测预警系统的设计与实现[D]. 北京: 中国地质大学(北京), 2017.

    ZHANG Biao. Design and implementation of tailings pond online safety monitoring and pre-warning system[D]. Beijing: China University of Geosciences, 2017.
    [2]
    KOMLJENOVIC D,STOJANOVIC L,MALBAI V,et al. A resilience-based approach in managing the closure and abandonment of large mine tailing ponds[J]. International Journal of Mining Science and Technology,2020,30(5):737-746. doi: 10.1016/j.ijmst.2020.05.007
    [3]
    国家安全生产监督管理总局. 尾矿库安全监督管理规定[2011]38号[Z]. 北京: 国家安全生产监督管理总局, 2011.

    State Administration of Work Safety. Regulations on safety supervision and management of tailings pond [2011]No.38[Z]. Beijing: State Administration of Work Safety, 2011.
    [4]
    GB 39496—2020 尾矿库安全规程[S].

    GB 39496-2020 Safety regulation for tailings pond[S].
    [5]
    JUNG C,MOON C B,JUNG D,et al. Design of test track for accurate calibration of two wheel differential mobile robots[J]. International Journal of Precision Engineering & Manufacturing,2014,15(1):53-61.
    [6]
    张胜宾,赵祚喜. 基于UMBmark算法的移动机器人定位试验研究[J]. 现代电子技术,2018,41(3):80-83.

    ZHANG Shengbin,ZHAO Zuoxi. Research on mobile robot localization experiment based on UMBmark algorithm[J]. Modern Electronics Technique,2018,41(3):80-83.
    [7]
    DANIEL D,JAN K,KAREL H,et al. Odometer module for mobile robot with position error estimation[J]. IFAC Papers OnLine,2016,49(25):346-351. doi: 10.1016/j.ifacol.2016.12.063
    [8]
    蔡李花,方海峰,李允旺,等. 矿井救灾机器人自主定位方法研究[J]. 工矿自动化,2015,41( 7):62-67.

    CAI Lihua,FANG Haifeng,LI Yunwang,et al. Research of self-localization method of mine rescue robot[J]. Industry and Mine Automation,2015,41( 7):62-67.
    [9]
    ZHANG Chao, ZHAN Quanzhong, WANG Qi, et al. Autonomous dam surveillance robot system based on multi-sensor fusion[J]. Sensors, 2020, 20( 4): 1097. DOI: 10.3390/s20041097.
    [10]
    杨林,马宏伟,王岩,等. 煤矿巡检机器人同步定位与地图构建方法研究[J]. 工矿自动化,2019,45( 9):18-24.

    YANG Lin,MA Hongwei,WANG Yan,et al. Research on method of simultaneous localization and mapping of coal mine inspection robot[J]. Industry and Mine Automation,2019,45( 9):18-24.
    [11]
    WEN Jingren, QIAN Chuang, TANG Jian, et al. 2D LiDAR SLAM back-end optimization with control network constraint for mobile mapping[J]. Sensors, 2018, 18(11): 3668. DOI: 10.3390/s18113668.
    [12]
    HESS W, KOHLER D, RAPP H, et al. Real-time loop closure in 2D LIDAR SLAM[C]// 2016 IEEE International Conference on Robotics and Automation (ICRA), 2016.
    [13]
    李猛钢. 煤矿救援机器人导航系统研究[D]. 徐州: 中国矿业大学, 2017.

    LI Menggang. Research on navigation system of coal mine rescue robot [D]. Xuzhou: China University of Mining and Technology, 2017.
    [14]
    邢伯阳,潘峰,冯肖雪. ArUco−SLAM: 基于ArUco二维码阵列的单目实时建图定位系统[J]. 北京理工大学学报,2020,40(4):427-433.

    XING Boyang,PAN Feng,FENG Xiaoxue. ArUco-SLAM: a monocular SLAM system based on ArUco landmark array[J]. Transactions of Beijing Institute of Technology,2020,40(4):427-433.
    [15]
    肖献强,程亚兵,王家恩. 基于惯性和视觉复合导航的自动导引小车研究与设计[J]. 中国机械工程,2019,30(22):2734-2740. doi: 10.3969/j.issn.1004-132X.2019.22.013

    XIAO Xianqiang,CHENG Yabing,WANG Jia'en. Research and design of AGVs based on inertial and visual composite navigation[J]. China Mechanical Engineering,2019,30(22):2734-2740. doi: 10.3969/j.issn.1004-132X.2019.22.013
    [16]
    KALLWIES J, FORKEL B, WUENSCHE H J. Determining and improving the localization accuracy of apriltag detection[C]// 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020.
    [17]
    ZHANG Z. A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(11):1330-1334. doi: 10.1109/34.888718
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(5)  / Tables(1)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (140) PDF downloads(17) Cited by()
    Proportional views
    Related

    苏ICP备 05016349号-2

    Supported by: Beijing Renhe Information Technology Co. Ltd.  Visits：    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return