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煤矿井下无轨胶轮车无人驾驶系统研究

周李兵

周李兵. 煤矿井下无轨胶轮车无人驾驶系统研究[J]. 工矿自动化,2022,48(6):36-48.  doi: 10.13272/j.issn.1671-251x.17946
引用本文: 周李兵. 煤矿井下无轨胶轮车无人驾驶系统研究[J]. 工矿自动化,2022,48(6):36-48.  doi: 10.13272/j.issn.1671-251x.17946
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

煤矿井下无轨胶轮车无人驾驶系统研究

doi: 10.13272/j.issn.1671-251x.17946
基金项目: 天地科技股份有限公司科技创新创业资金专项项目(2021-TD-ZD004);中煤科工集团常州研究院有限公司科研项目(2022TY6001);天地(常州)自动化股份有限公司科研项目(2022FY0003)。
详细信息
    作者简介:

    周李兵(1984—),男,湖北黄梅人,高级工程师,硕士,主要从事矿山自动化与信息化方面的研究与应用工作,E-mail:15295023477@126.com

  • 中图分类号: TD525/67

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

  • 摘要: 煤矿井下无轨胶轮车无人驾驶可大幅减少井下辅助运输作业人员数量,降低人员劳动强度,是辅助运输智能化的主要发展方向之一。相较于地面汽车无人驾驶,煤矿井下无轨胶轮车无人驾驶存在一系列新的挑战:井下巷道“长廊效应”、“多径效应”干扰;狭窄场景内人车混行等复杂路况对车辆精准控制的高要求;井下卫星拒止环境带来的定位问题;井下光照多变且巷道壁阻挡影响机器视觉的应用;设备需满足MA认证;安全措施需多重冗余设计等。针对上述挑战,提出了以车联网为核心的煤矿井下无轨胶轮车无人驾驶系统架构,分析了系统实现的关键技术:利用基于激光同步定位与建图(SLAM)和超宽带(UWB)/惯性导航系统(INS)的组合定位方式,实现车辆高速移动状态下的精确定位;依托车身多传感器(毫米波雷达、激光雷达、超声波雷达、摄像头)、矿用智能路侧单元等识别车身周边路况信息,并通过车联网共享相关信息;利用多源数据采集技术获得环境感知数据、车辆运行数据、路侧监控数据、移动目标数据,海量数据经5G等无线通信网络交互至基于边缘计算的分布式算力单元融合分析后,结合全局和局部路径规划算法合理规划车辆行驶路径,实现仓库管理系统化的车辆智能调度;考虑到井下机电设备安全准入要求,感知、线控、决策控制装备需实现矿用化设计且应尽量采用矿用本安型产品,以满足成本低、体积小、效率高的设计需求;井下无人驾驶车辆需实现感知、决策与控制环节的冗余设计,以实现非正常状况下车辆的安全可靠控制。现场测试结果表明:车辆定位精度可达0.3 m,通信带宽≥50 Mbit/s,数据通信时延≤50 ms,定位精度和数据交互满足井下无人驾驶基本需求;针对T形支巷及U型弯道等典型环境可实现避障及连续路径规划;基于多传感器融合策略,可实现多种目标感知能力提升;车辆动态跟随误差<0.54 m/s,垂直于巷道壁方向平均控制误差<0.2 m,满足无人驾驶车辆的控制要求。

     

  • 图  1  煤矿井下无轨胶轮车无人驾驶系统架构

    Figure  1.  Unmanned driving system architecture of trackless rubber-tyred vehicle in underground coal mine

    图  2  基于多传感器融合的井下车辆路况识别

    Figure  2.  Road condition recognition of underground vehicle based on multi-sensor fusion

    图  3  多源数据采集技术

    Figure  3.  Multi-source data acquisition technology

    图  4  无人驾驶数据交互网

    Figure  4.  Data interaction network of unmanned driving

    图  5  井下车辆智能调度工作机制

    Figure  5.  Working mechanism of intelligent dispatching of underground vehicle

    图  6  矿用本安型高性能边缘计算装备电气原理

    Figure  6.  Electrical principle of mine intrinsically safe high-performance edge computing equipment

    图  7  井下C−V2X

    Figure  7.  Underground C−V2X

    图  8  井下无人驾驶无轨胶轮车的权限管控及运转流程

    Figure  8.  Authorization control and operation process of underground unmanned driving trackless rubber-tyred vehicle

    图  9  决策控制主机本安化设计方案

    Figure  9.  Intrinsically safety design scheme of decision control host

    图  10  车辆静态定位数据

    Figure  10.  Positioning data of static vehicle

    图  11  车辆动态定位数据

    Figure  11.  Positioning data of dynamic vehicle

    图  12  无人驾驶车辆避障结果

    Figure  12.  Obstacle avoidance results of unmanned driving vehicle

    图  13  基于不同传感器的目标障碍物检测结果

    Figure  13.  Detection results of target obstacle by different sensors

    图  14  无人驾驶车辆横纵向控制测试结果

    Figure  14.  Test results of lateral and longitudinal control of unmanned driving vehicle

    表  1  室内定位技术对比

    Table  1.   Comparison of indoor positioning technologies

    技术精度/m能耗传输距离/m抗干扰能力优点缺点
    蓝牙3.0100较弱功耗低,穿透力较强抗干扰能力较弱,定位精度较差
    UWB0.3较低250抗干扰能力强,定位精度较高需额外设备,部署较难
    RFID1.0~5.05功耗低,数据传输速率高无法连续定位,定位精度差
    ZigBee3.0较低75成本及功耗较低定位精度较差,抗干扰能力较弱
    WiFi5.0较低50较弱成本低,方便部署定位精度差
    激光0.1300定位精度高,抗干扰能力强功耗大,只可在可视范围内测距
    超声波1.0较低10功耗较低,抗干扰能力强有效定位距离短,部署难
    下载: 导出CSV

    表  2  常用路径规划算法优缺点及适用环境

    Table  2.   Advantages, disadvantages and applicable environments of common path planning algorithms

    算法优点缺点适用环境
    Dijkstra算法 使用贪心策略选择最优节点,能获得最优路径 运算过程中会占用大量计算资源,规划效率较低 环境信息已知的全局路径规划
    A*算法 通过启发式采样方式搜索节点,算法搜索效率高 对地图要求较高,无法保证得到最优解 环境信息已知的全局路径规划
    快速搜索随机树算法 通过随机采样搜索节点,适用于非完整约束场合 生成路径非最优,且规划效率低 环境信息已知的全局路径规划
    人工势场法 能够实时避障,生成的路径平滑、安全 障碍物较多时容易陷入局部最优,造成目标不可达 既可用于全局路径规划,也可用于
    局部路径规划
    动态窗口法 能够考虑无人驾驶车辆的速度与运动学约束,生成的路径平滑 存在局部最优解,且在复杂环境下计算复杂度高 环境信息部分已知的局部路径规划
    蚁群算法 容易与其他启发式算法结合,能寻找到全局最优解 搜索具有盲目性,容易陷入局部最优 环境信息部分已知的局部路径规划
    下载: 导出CSV

    表  3  C−V2X通信时延及带宽

    Table  3.   Communication delay and bandwidth of C−V2X

    交互模式通信时延/ms通信带宽/(Mbit${\boldsymbol{\cdot}} $s−1
    V2N 上行 30~50 150~200
    下行 20~30 50~100
    V2I 上行 10~20 100~150
    下行 5~15 50~80
    下载: 导出CSV

    表  4  井下目标障碍物感知统计结果

    Table  4.   Statistical results of underground target obstacles perception

    目标类型目标个数准确率/%
    毫米波
    雷达
    激光
    雷达
    摄像头多传感器融合
    大目标39096.395.993.798.9
    中目标35094.291.293.298.6
    小目标30086.583.589.996.5
    极小目标10081.978.488.594.8
    下载: 导出CSV
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  • 煤矿井下无轨胶轮车无人驾驶系统研究+增强视频.mp4
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出版历程
  • 收稿日期:  2022-05-10
  • 修回日期:  2022-06-23
  • 网络出版日期:  2022-06-28

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