Volume 48 Issue 6
Jun.  2022
Turn off MathJax
Article Contents
ZHOU Libing. Research on unmanned driving system of underground trackless rubber-tyred vehicle in coal mine[J]. Journal of Mine Automation,2022,48(6):36-48.  doi: 10.13272/j.issn.1671-251x.17946
Citation: ZHOU Libing. Research on unmanned driving system of underground trackless rubber-tyred vehicle in coal mine[J]. Journal of Mine Automation,2022,48(6):36-48.  doi: 10.13272/j.issn.1671-251x.17946

Research on unmanned driving system of underground trackless rubber-tyred vehicle in coal mine

doi: 10.13272/j.issn.1671-251x.17946
  • Received Date: 2022-05-10
  • Rev Recd Date: 2022-06-23
  • Available Online: 2022-06-28
  • The unmanned driving of underground trackless rubber-tyred vehicles in coal mine can significantly reduce the number of underground auxiliary transportation operating personnel, and reduce labor intensity. It is one of the leading development directions of intelligent auxiliary transportation. Compared with the unmanned driving of the ground vehicles, there are a series of new challenges for unmanned driving of underground trackless rubber-tyred vehicles. There is the interference of 'corridor effect' and 'multipath effect' in the underground roadway. There are high requirements for precise vehicle control under complex road conditions such as mixed traffic in narrow scenes. The underground satellite refusal environment causes positioning problems. Machine vision application is affected by the changeable illumination underground and the blocking of the roadway wall. The equipment shall meet MA certification. Multiple redundancy design is required for safety measures. In order to solve the above challenges, the architecture of the unmanned driving system for underground trackless rubber-tyred vehicle in coal mine based on the vehicle-to-everything is proposed. And the critical technologies of system implementation are analyzed. The integrated positioning method based on simultaneous localization and mapping (SLAM) and ultra wide band (UWB)/inertial navigation system (INS) is used to realize the precise positioning of the vehicle in the state of high-speed movement. By relying on the multi-sensor (millimeter-wave radar, laser radar, ultrasonic radar, camera) of the vehicle body and mining intelligent roadside unit, the road condition information around the vehicle body is identified. Through the vehicle-to-everything, the relevant information is shared. The multi-source data acquisition technology is used to obtain environmental perception data, vehicle operation data, roadside monitoring data, and mobile target data. The massive data is exchanged through 5G and other wireless communication networks to the distributed computing unit based on edge computing for fusion analysis. The vehicle driving path is reasonably planned in combination with global and local path planning algorithms to realize the systematic vehicle intelligent scheduling of warehouse management. Considering the safety access requirements of underground electromechanical equipment, the perception, wire control and decision-making control equipment shall be designed for mining. The mining intrinsically safe products shall be used as far as possible to meet the design requirements of low cost, small volume and high efficiency. Underground unmanned driving vehicles need to realize the redundant design of perception, decision-making and control links to realize the safe and reliable control of vehicles under abnormal conditions. The field test results show that the vehicle positioning precision can reach 0.3 m. The communication bandwidth is more than or equal to 50 Mbit/s. The data communication delay is less than or equal to 50 ms. Therefore the positioning precision and data exchange can meet the basic requirements of underground unmanned driving vehicles. The obstacle avoidance and continuous path planning can be realized for typical environments such as T-shaped roadway and U-shaped curve. Based on the multi-sensor fusion strategy, the perception capability of multiple targets can be improved. The vehicle dynamic following error is less than 0.54 m/s, and the average control error perpendicular to the roadway wall is less than 0.2 m. These results meet the control requirements of unmanned driving vehicles.

     

  • loading
  • [1]
    王国法. 煤矿智能化最新技术进展与问题探讨[J]. 煤炭科学技术,2022,50(1):1-27.

    WANG Guofa. New technological progress of coal mine intelligence and its problems[J]. Coal Science and Technology,2022,50(1):1-27.
    [2]
    倪兴华. 安全高效矿井辅助运输关键技术研究与应用[J]. 煤炭学报,2010,35(11):1909-1915. doi: 10.13225/j.cnki.jccs.2010.11.027

    NI Xinghua. Research and application of key technology for safety and high efficient mine auxiliary transportation[J]. Journal of China Coal Society,2010,35(11):1909-1915. doi: 10.13225/j.cnki.jccs.2010.11.027
    [3]
    游小荣,裴浩,霍振龙. 一种基于UWB的三边定位改进算法[J]. 工矿自动化,2019,45(11):19-23. doi: 10.13272/j.issn.1671-251x.2019050081

    YOU Xiaorong,PEI Hao,HUO Zhenlong. An improved trilateral positioning algorithm based on UWB[J]. Industry and Mine Automation,2019,45(11):19-23. doi: 10.13272/j.issn.1671-251x.2019050081
    [4]
    GITHINJI L. Effect of biochar application rate on soil physical and hydraulic properties of a sandy loam[J]. Archives of Agronomy and Soil Science,2014,60(4):457-470. doi: 10.1080/03650340.2013.821698
    [5]
    SHAN Tixiao, ENGLOT B. LeGO-LOAM: lightweight and ground-optimized lidar odometry and mapping on variable terrain[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems, Madrid, 2019: 4758-4765.
    [6]
    ASSEM I,DUPONT G. Friezes and a construction of the Euclidean cluster variables[J]. Journal of Pure and Applied Algebra,2011,215(10):2322-2340. doi: 10.1016/j.jpaa.2010.12.013
    [7]
    LIU Yongfan,DU Sen,KONG Youyong. Supervoxel clustering with a novel 3D descriptor for brain tissue segmentation[J]. International Journal of Machine Learning and Computing,2020,10(3):501-506. doi: 10.18178/ijmlc.2020.10.3.964
    [8]
    CHEN Yizhou,SHEN Shifei,CHEN Tao,et al. Path optimization study for vehicles evacuation based on Dijkstra algorithm[J]. Procedia Engineering,2014,71:159-165. doi: 10.1016/j.proeng.2014.04.023
    [9]
    鲍久圣,张牧野,葛世荣,等. 基于改进A*和人工势场算法的无轨胶轮车井下无人驾驶路径规划[J]. 煤炭学报,2022,47(3):1347-1360. doi: 10.13225/j.cnki.jccs.xr21.1716

    BAO Jiusheng,ZHANG Muye,GE Shirong,et al. Underground driverless path planning of trackless rubber tyred vehicle based on improved A* and artificial potential field algorithm[J]. Journal of China Coal Society,2022,47(3):1347-1360. doi: 10.13225/j.cnki.jccs.xr21.1716
    [10]
    ZHANG Zhenghao,QIAO Bing,ZHAO Wentong,et al. A predictive path planning algorithm for mobile robot in dynamic environments based on rapidly exploring random tree[J]. Arabian Journal for Science and Engineering,2021,46(9):8223-8232. doi: 10.1007/s13369-021-05443-8
    [11]
    田子建,高学浩,张梦霞. 基于改进人工势场的矿井导航装置路径规划[J]. 煤炭学报,2016,41(增刊2):589-597. doi: 10.13225/j.cnki.jccs.2016.1165

    TIAN Zijian,GAO Xuehao,ZHANG Mengxia. Path planning based on the improved artificial potential field of coal mine dynamic target navigation[J]. Journal of China Coal Society,2016,41(S2):589-597. doi: 10.13225/j.cnki.jccs.2016.1165
    [12]
    袁晓明,郝明锐. 煤矿无轨辅助运输无人驾驶关键技术与发展趋势研究[J]. 智能矿山,2020,1(1):89-97.

    YUAN Xiaoming,HAO Mingrui. Key technology and development trend of mine auxiliary transport autonomous vehicle[J]. Journal of Intelligent Mine,2020,1(1):89-97.
    [13]
    谭玉新,杨维,徐子睿. 面向煤矿井下局部复杂空间的机器人三维路径规划方法[J]. 煤炭学报,2017,42(6):1634-1642. doi: 10.13225/j.cnki.jccs.2016.1047

    TAN Yuxin,YANG Wei,XU Zirui. Three-dimensional path planning method for robot in underground local complex space[J]. Journal of China Coal Society,2017,42(6):1634-1642. doi: 10.13225/j.cnki.jccs.2016.1047
    [14]
    LIU Jianhua,YANG Jianguo,LIU Huaping,et al. An improved ant colony algorithm for robot path planning[J]. Soft Computing,2017,21(19):5829-5839. doi: 10.1007/s00500-016-2161-7
    [15]
    张朝阳. 矿用无轨胶轮车无人驾驶系统研究[D]. 西安: 西安科技大学, 2016.

    ZHANG Chaoyang. Research on unmanned system for mine trackless rubber wheel vehicle[D]. Xi'an: Xi'an University of Science and Technology, 2016.
    [16]
    周晶晶, 苏致远, 马育林, 等. 基于多传感器的智能车交通状态感知关键技术研究[C]//第11届中国智能交通年会大会, 重庆, 2016: 688-692.

    ZHOU Jingjing, SU Zhiyuan, MA Yulin, et al. Research on key technology of intelligent vehicle traffic state perception based on multi-sensor[C]//The 11th China Intelligent Transportation Annual Conference, Chongqing, 2016: 688-692.
    [17]
    任大凯,廖振松. 5G车路协同自动驾驶应用研究[J]. 电信工程技术与标准化,2020,33(9):68-74. doi: 10.3969/j.issn.1008-5599.2020.09.014

    REN Dakai,LIAO Zhensong. Research on application of 5G-V2X autonomous driving[J]. Telecom Engineering Technics and Standardization,2020,33(9):68-74. doi: 10.3969/j.issn.1008-5599.2020.09.014
    [18]
    王斌. 煤矿无轨辅助运输设备的应用与发展趋势[J]. 煤矿机械,2013,34(8):1-3. doi: 10.13436/j.mkjx.2013.08.117

    WANG Bin. Application and development of coal mine trackless auxiliary transportation equipment[J]. Coal Mine Machinery,2013,34(8):1-3. doi: 10.13436/j.mkjx.2013.08.117
    [19]
    刘宏杰,张慧,张喜麟,等. 煤矿无轨胶轮车智能调度管理技术研究与应用[J]. 煤炭科学技术,2019,47(3):81-86. doi: 10.13199/j.cnki.cst.2019.03.011

    LIU Hongjie,ZHANG Hui,ZHANG Xilin,et al. Research and application of intelligent dispatching and management technology for coal mine trackless rubber-tyred vehicle[J]. Coal Science and Technology,2019,47(3):81-86. doi: 10.13199/j.cnki.cst.2019.03.011
    [20]
    李建明. 梅花井煤矿辅助运输系统选择及应用研究[J]. 煤炭科技,2014,35(3):1-2. doi: 10.3969/j.issn.1008-3731.2014.03.002

    LI Jianming. Research on selection and application of auxiliary transportation system in Meihuajing Coal Mine[J]. Coal Science & Technology Magazine,2014,35(3):1-2. doi: 10.3969/j.issn.1008-3731.2014.03.002
    [21]
    吴建波,朱文霞,剧亮,等. 边缘计算在智慧交通系统中的应用[J]. 计算机与现代化,2021(12):103-109,122. doi: 10.3969/j.issn.1006-2475.2021.12.017

    WU Jianbo,ZHU Wenxia,JU Liang,et al. Application of edge computing in intelligent transportation systems[J]. Computer and Modernization,2021(12):103-109,122. doi: 10.3969/j.issn.1006-2475.2021.12.017
    [22]
    陈霄,刘巍,陈静,等. 边缘计算环境下的计算卸载策略研究[J]. 火力与指挥控制,2022,47(1):7-14,19. doi: 10.3969/j.issn.1002-0640.2022.01.002

    CHEN Xiao,LIU Wei,CHEN Jing,et al. Research on computing offload strategy in edge computing environment[J]. Fire Control & Command Control,2022,47(1):7-14,19. doi: 10.3969/j.issn.1002-0640.2022.01.002
    [23]
    杨晓丹. 煤矿井下防爆电气设备中的应用技术[J]. 电子技术与软件工程,2019(24):223-224.

    YANG Xiaodan. Application technology of explosion-proof electrical equipment in coal mine[J]. Electronic Technology & Software Engineering,2019(24):223-224.
    [24]
    陈山枝,时岩,胡金玲. 蜂窝车联网(C−V2X)综述[J]. 中国科学基金,2020,34(2):179-185. doi: 10.16262/j.cnki.1000-8217.2020.02.009

    CHEN Shanzhi,SHI Yan,HU Jinling. Cellular vehicle to everything(C-V2X):a review[J]. Bulletin of National Natural Science Foundation of China,2020,34(2):179-185. doi: 10.16262/j.cnki.1000-8217.2020.02.009
    [25]
    陈山枝,葛雨明,时岩. 蜂窝车联网(C−V2X)技术发展、应用及展望[J]. 电信科学,2022,38(1):1-12.

    CHEN Shanzhi,GE Yuming,SHI Yan. Technology development,application and prospect of cellular vehicle-to-everything(C-V2X)[J]. Telecommunications Science,2022,38(1):1-12.
    [26]
    阎俊豪,贾宗璞,李东印. 智能矿山车联网体系架构与关键技术[J]. 煤炭科学技术,2020,48(7):249-254. doi: 10.13199/j.cnki.cst.2020.07.026

    YAN Junhao,JIA Zongpu,LI Dongyin. Architecture and key technologies of intelligent of vehicles in intelligent mine[J]. Coal Science and Technology,2020,48(7):249-254. doi: 10.13199/j.cnki.cst.2020.07.026
    [27]
    韩江洪,卫星,陆阳,等. 煤矿井下机车无人驾驶系统关键技术[J]. 煤炭学报,2020,45(6):2104-2115. doi: 10.13225/j.cnki.jccs.ZN20.0343

    HAN Jianghong,WEI Xing,LU Yang,et al. Driverless technology of underground locomotive in coal mine[J]. Journal of China Coal Society,2020,45(6):2104-2115. doi: 10.13225/j.cnki.jccs.ZN20.0343
    [28]
    闫凌,黄佳德. 矿用卡车无人驾驶系统研究[J]. 工矿自动化,2021,47(4):19-29. doi: 10.13272/j.issn.1671-251x.17729

    YAN Ling,HUANG Jiade. Research on unmanned driving system of mine-used truck[J]. Industry and Mine Automation,2021,47(4):19-29. doi: 10.13272/j.issn.1671-251x.17729
    [29]
    于月森,谢冬莹,李世光,等. 本质安全电路技术综述[J]. 煤炭科学技术,2011,39(6):61-65. doi: 10.13199/j.cst.2011.06.67.yuys.025

    YU Yuesen,XIE Dongying,LI Shiguang,et al. Summary of intrinsic safety electric circuit technology[J]. Coal Science and Technology,2011,39(6):61-65. doi: 10.13199/j.cst.2011.06.67.yuys.025
    [30]
    林引. 矿用高可靠性本安型传感器电源电路设计与实现[J]. 煤炭科学技术,2013,41(6):88-91.

    LIN Yin. Design and realization on power of high reliable intrinsic safe sensor[J]. Coal Science and Technology,2013,41(6):88-91.
    [31]
    王璇. 矿用本安型网口电路设计[J]. 煤矿安全,2016,47(6):113-114,118. doi: 10.13347/j.cnki.mkaq.2016.06.031

    WANG Xuan. Design of mine-used intrinsic safe network interface circuit[J]. Safety in Coal Mines,2016,47(6):113-114,118. doi: 10.13347/j.cnki.mkaq.2016.06.031
  • 煤矿井下无轨胶轮车无人驾驶系统研究+增强视频.mp4
  • 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(14)  / Tables(4)

    Article Metrics

    Article views (484) PDF downloads(91) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return