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煤矿带式输送机分拣机器人异物识别与定位系统设计

薛旭升 杨星云 齐广浩 马宏伟 毛清华 尚新芒

薛旭升,杨星云,齐广浩,等. 煤矿带式输送机分拣机器人异物识别与定位系统设计[J]. 工矿自动化,2022,48(12):33-41.  doi: 10.13272/j.issn.1671-251x.2022100024
引用本文: 薛旭升,杨星云,齐广浩,等. 煤矿带式输送机分拣机器人异物识别与定位系统设计[J]. 工矿自动化,2022,48(12):33-41.  doi: 10.13272/j.issn.1671-251x.2022100024
XUE Xusheng, YANG Xingyun, QI Guanghao, et al. Design of foreign object recognition and positioning system for sorting robot of coal mine belt conveyor[J]. Journal of Mine Automation,2022,48(12):33-41.  doi: 10.13272/j.issn.1671-251x.2022100024
Citation: XUE Xusheng, YANG Xingyun, QI Guanghao, et al. Design of foreign object recognition and positioning system for sorting robot of coal mine belt conveyor[J]. Journal of Mine Automation,2022,48(12):33-41.  doi: 10.13272/j.issn.1671-251x.2022100024

煤矿带式输送机分拣机器人异物识别与定位系统设计

doi: 10.13272/j.issn.1671-251x.2022100024
基金项目: 国家重点研发计划青年科学家项目(2022YFF0605300);国家自然科学基金面上项目(51975468);陕西省自然科学基础研究计划项目(2019JQ-802);国家自然科学基金重点项目(51834006);西安市科技计划项目(22GXFW0067)。
详细信息
    作者简介:

    薛旭升(1987—),男,陕西兴平人,讲师,博士,主要研究方向为智能检测与控制、煤矿机器人关键技术等,E-mail:xuexsh@xust.edu.cn

    通讯作者:

    杨星云(1995—),男,山西朔州人,硕士研究生,主要研究方向为机械电子工程,E-mail:1343034867@qq.com

  • 中图分类号: TD634

Design of foreign object recognition and positioning system for sorting robot of coal mine belt conveyor

  • 摘要: 机器视觉已在煤矿带式输送机分拣机器人目标检测与识别方面具有一定的理论基础,但目前煤矿带式输送机分拣机器人目标识别主要针对煤矸石识别,对造成输送带穿透、撕裂等的异物目标识别的研究较少,且在目标异物精确定位方面的研究也较少。针对上述问题,设计了一种基于机器视觉的煤矿带式输送机分拣机器人异物识别与定位系统,可对输送带上存在的不同类型和不同形状的异物进行识别与定位。采用双目视觉实时获取输送带上异物图像信息,并对图像进行预处理,基于Canny算子进行图像信息增强,通过灰度拉伸方法改进图像边缘信息,突出煤矿带式输送机上异物的边缘特征;利用形态学方法提取异物形状特征,建立异物图像特征样本库,通过图像特征匹配的方式解算出异物存在区域,实现异物类型的检测、分类与识别;在异物类型成功识别的基础上,以目标异物边缘特征值为基础,建立目标异物的感兴趣区域(ROI),构建相机、输送带与目标异物坐标转换关系,利用多目标质心快速计算方法求取目标异物质心坐标,实现对目标异物的定位。系统样机实验结果表明:煤矿带式输送机分拣机器人异物识别与定位系统异物识别率不受尺寸、材质和颜色等因素影响,能够实现输送带目标异物图像的采集、处理、特征提取、识别和位置定位,识别率为92.5 %以上,目标异物位置定位平均误差为3%左右。

     

  • 图  1  煤矿带式输送机分拣机器人系统样机

    Figure  1.  Prototype of sorting robot system of coal mine belt conveyor

    图  2  煤矿带式输送机分拣机器人异物识别与定位系统界面

    Figure  2.  Interface of foreign object recognition and positioning system for sorting robot of coal mine belt conveyor

    图  3  煤矿带式输送机分拣机器人异物识别和定位方法架构

    Figure  3.  Architecture of foreign object recognition and positioning method for sorting robots of coal mine belt conveyor

    图  4  异物识别方法原理

    Figure  4.  Principle of foreign object recognition methods

    图  5  ROI视觉内划定

    Figure  5.  Intra-visual delineation of region of interest

    图  6  图像坐标系和像素坐标系

    Figure  6.  Image and pixel coordinate systems

    图  7  转换坐标系

    Figure  7.  Converting the coordinate system

    图  8  目标异物坐标信息、目标异物类别信息、输送带实时图像和目标识别提取图像等系统程序

    Figure  8.  System programs such as target foreign object coordinate information, target foreign object category information, real-time image of conveyor belt and target recognition and extraction image

    图  9  煤矿带式输送机分拣机器人异物识别与定位实验

    Figure  9.  Foreign object recognition and positioning experiment of sorting robot of coal mine belt conveyor

    图  10  不同形状的杆状目标异物

    Figure  10.  Rod-shaped target foreign object with different shape

    图  11  目标异物X,Y轴坐标误差

    Figure  11.  Target foreign object coordinates X and Y axis error

    表  1  输送带上的异物类别

    Table  1.   Types of foreign objects on conveyor belt

    序号异物序号异物序号异物
    18铁背板15道钉/道木
    2(半)圆木9竹芭/塑芭16道夹板
    3锚杆/锚索10工字钢头17风带条
    4铁丝11钻头/杆18钢丝绳
    5编织袋12输送带头/卡19塑料瓶/袋
    6棉纱13脚线20盖板
    7螺栓、螺帽14雷管21水/水煤
    下载: 导出CSV

    表  2  实验平台主要性能参数

    Table  2.   Main performance parameters of the experimental platform

    序号核心部件主要参数
    1 双目视觉 视角:对角线视角为121°;水平方向视角为105°;
    垂直方向视角为58°
    焦距/mm: 2.45
    深度工作距离/m: 0.32~7
    同步精度/ms: <0.01
    帧率/(帧·s−1): 60
    分辨率: 2 560×720
    像素尺寸/μm: 3.75×3.75
    2 机械臂 最大伸展距离/mm: 320
    重复定位精度/mm: 0.2
    抓取范围/mm: 310
    抓取效率/%: >95
    3 输送带 长度/mm: 600
    速度/(mm·s−1): 120
    下载: 导出CSV

    表  3  异物图像识别结果

    Table  3.   Foreign object image recognition results

    样本类别样本数正确识别样本数识别率/%
    杆状异物302893.33
    下载: 导出CSV

    表  4  不同长度的杆状目标异物图像识别结果

    Table  4.   Rod-shaped target foreign object image recognition results for different lengths

    长度/cm实验次数成功识别次数识别率/%
    3403997.5
    5403792.5
    7403895.0
    下载: 导出CSV

    表  5  不同直径的杆状目标异物图像识别结果

    Table  5.   Rod-shaped target foreign object image recognition results for different diameters

    直径/mm实验次数成功识别次数识别率/%
    15403895.0
    10403792.5
    5403895.0
    下载: 导出CSV

    表  6  目标异物质心位置坐标解算实验结果

    Table  6.   Experimental results for solving the coordinates of the target foreign object's centre of mass position

    目标异物x0/mm${x}_{0}'$/mm相对误差/%y0/mm${y}_{0}'$/mm相对误差/%
    16059.784 90.366362.928 30.11
    25857.016 91.705051.235 72.47
    34846.269 73.606563.852 81.76
    45552.932 53.765956.740 93.83
    55046.337 77.326161.080 30.13
    65656.598 01.076161.858 51.41
    75048.676 02.656463.497 50.79
    85859.072 51.855455.921 93.56
    94846.701 12.715554.869 20.24
    104340.729 25.285350.847 24.06
    114445.499 73.415957.838 91.97
    125859.908 93.296263.018 71.64
    134344.003 92.335962.768 56.39
    144748.606 13.425457.232 85.99
    154948.010 22.026561.735 95.02
    165352.759 70.455855.509 94.29
    175957.437 42.655754.312 74.71
    185958.117 01.505759.562 64.50
    194745.203 43.825551.423 46.50
    204241.205 41.895855.304 44.65
    下载: 导出CSV
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  • 收稿日期:  2022-10-12
  • 修回日期:  2022-12-05
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