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矿井无人驾驶环境感知技术研究现状及展望

胡青松 孟春蕾 李世银 孙彦景

胡青松,孟春蕾,李世银,等. 矿井无人驾驶环境感知技术研究现状及展望[J]. 工矿自动化,2023,49(6):128-140.  doi: 10.13272/j.issn.1671-251x.18115
引用本文: 胡青松,孟春蕾,李世银,等. 矿井无人驾驶环境感知技术研究现状及展望[J]. 工矿自动化,2023,49(6):128-140.  doi: 10.13272/j.issn.1671-251x.18115
HU Qingsong, MENG Chunlei, LI Shiyin, et al. Research status and prospects of perception technology for unmanned mining vehicle driving environment[J]. Journal of Mine Automation,2023,49(6):128-140.  doi: 10.13272/j.issn.1671-251x.18115
Citation: HU Qingsong, MENG Chunlei, LI Shiyin, et al. Research status and prospects of perception technology for unmanned mining vehicle driving environment[J]. Journal of Mine Automation,2023,49(6):128-140.  doi: 10.13272/j.issn.1671-251x.18115

矿井无人驾驶环境感知技术研究现状及展望

doi: 10.13272/j.issn.1671-251x.18115
基金项目: 国家自然科学基金资助项目(51874299);中国矿业大学“双一流”建设提升自主创新能力项目(2022ZZCX01K01);山东省重大科技创新工程项目(2019JZZY020505);中国矿业大学“工业物联网与应急协同”创新团队资助计划项目(2020ZY002)。
详细信息
    作者简介:

    胡青松(1978—),男,四川岳池人,教授,博士,博士研究生导师,研究方向为目标定位、矿山物联网和救灾通信,E-mail:hqsong722@163.com

  • 中图分类号: TD67

Research status and prospects of perception technology for unmanned mining vehicle driving environment

  • 摘要: 矿井辅助运输系统是煤矿企业运输人员和重要物料、装备的必备系统,实现矿井无人驾驶是提高运输效率、保障运输安全的必然要求,也是落实国家煤矿智能化建设部署的必由之路。矿井无人驾驶依赖于准确实时的环境感知,即利用激光雷达、毫米波雷达等车载感知器件和车联网支持下的协同感知,实现车辆局部甚至矿井全局的精确详尽感知。对矿井无人驾驶环境感知技术的研究现状进行了系统梳理,指出巷道特殊环境使得矿井车载感知设备的性能都将出现不同程度的下降,并对各种车载感知设备的优劣进行了总结归纳;详细阐述了矿井无人驾驶环境感知的关键技术,包括基于可见光图像或激光点云的单传感器障碍物识别方法,多传感器融合感知的分类及可见光图像+激光点云、可见光图像+毫米波点云、可见光图像+激光点云+毫米波点云、4D毫米波雷达+其他感知器件等多传感器融合方式,智能网联协同感知的实现方式、数据处理方法及其对无人驾驶的促进作用,井下巷道交通标志检测与识别方法,井下无轨胶轮车和有轨机车的巷道可行驶区域分割方法等;对矿井无人驾驶环境感知技术的发展方向进行了展望,建议提高矿井多传感器融合性能、研究矿井自适应感知算法并突破矿井智能网联协同感知技术。

     

  • 图  1  矿井无人驾驶环境感知关键技术

    Figure  1.  Key perception technologies of unmanned mining vehicle driving environment

    图  2  点云数据的不同表达形式

    Figure  2.  Different expressions of point cloud data

    图  3  多传感器数据融合方式

    Figure  3.  Multi-sensor data fusion methods

    图  4  融合可见光图像与激光点云的3D目标检测模型

    Figure  4.  3D target detection model based on the fusion of visible light mage and laser point cloud

    图  5  图像与激光点云融合感知路况

    Figure  5.  Perception of road conditions based on fusion of image and laser point cloud

    图  6  毫米波雷达与可见光摄像机两级融合策略

    Figure  6.  Two level fusion strategy for millimeter wave radar and visible light camera

    图  7  基于多传感器融合的井下车辆路况识别

    Figure  7.  Underground vehicle road condition recognition based on multi-sensor fusion

    图  8  基于传统图像处理的轨道检测算法一般流程

    Figure  8.  General process of track inspection algorithm based on traditional image processing

    图  9  轨道线检测流程

    Figure  9.  Track line inspection process

    表  1  常用的矿井车载感知器件作用及其优劣

    Table  1.   Functions of common mine vehicle perception devices and their advantages and disadvantages

    感知器件名称作用优点缺点
    激光雷达 生成实时点云地图,障碍物识别与测距 测距精度高,分辨率高 受粉尘影响大,价格高,体积大
    毫米波雷达 障碍物测距 不受光照影响,受粉尘影响小,测距测速精度高,体积小,成本低 分辨率低,检测距离短,难以区分物体形状
    4D毫米波
    雷达
    障碍物识别与测距 不受光照影响,受粉尘影响小,测距测速精度高,角分辨率高,可测高成像,探测距离远,价格适中 点云稀疏
    超声波雷达 测量矿车与巷道壁的距离,短距离防碰撞 近距离测量优势大,成本低 方向性差,回波信号弱,检测距离短
    可见光
    摄像机
    环境图像信息采集,障碍物识别与测距 分辨率高,有颜色信息,体积小,成本低 难以测距,受光照、粉尘影响大,可靠性低
    红外摄像机 障碍物识别 受光照、粉尘影响小 分辨率低,探测距离近
    深度相机 获取前方物体深度,障碍物识别与测距 有配套算法进行解析,使用方便 获取信息少,信息易偏移,受光照、粉尘影响大
    双目视觉相机 获取边缘信息,进行场景重构,障碍物识别与测距 可测距,分辨率高,有颜色信息,体积小,成本低 受光照、粉尘影响大,可靠性低
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  • 收稿日期:  2023-05-07
  • 修回日期:  2023-06-01
  • 网络出版日期:  2023-06-30

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