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基于多激光传感器装配的自由曲面法线找正方法研究

张赢 丁红昌 赵长福 周义根 曹国华

张赢, 丁红昌, 赵长福, 周义根, 曹国华. 基于多激光传感器装配的自由曲面法线找正方法研究[J]. 中国光学, 2021, 14(2): 344-352. doi: 10.37188/CO.2020-0205
引用本文: 张赢, 丁红昌, 赵长福, 周义根, 曹国华. 基于多激光传感器装配的自由曲面法线找正方法研究[J]. 中国光学, 2021, 14(2): 344-352. doi: 10.37188/CO.2020-0205
ZHANG Ying, DING Hong-chang, ZHAO Chang-fu, ZHOU Yi-gen, CAO Guo-hua. The normal alignment method for freeform surfaces based on multiple laser sensor assembly[J]. Chinese Optics, 2021, 14(2): 344-352. doi: 10.37188/CO.2020-0205
Citation: ZHANG Ying, DING Hong-chang, ZHAO Chang-fu, ZHOU Yi-gen, CAO Guo-hua. The normal alignment method for freeform surfaces based on multiple laser sensor assembly[J]. Chinese Optics, 2021, 14(2): 344-352. doi: 10.37188/CO.2020-0205

基于多激光传感器装配的自由曲面法线找正方法研究

doi: 10.37188/CO.2020-0205
基金项目: 国家重点研发计划重大科学仪器开发项目(No. 2017YFF0105304);吉林省省级产业创新专项资金项目(No. 2018C038-4);吉林省科技研发计划项目(No. 20200401117GX)
详细信息
    作者简介:

    张赢:张 赢(1984—),男,吉林长春人,博士研究生,2007年于长春理工大学获得学士学位;2010年于陕西科技大学获得硕士学位。主要从事光学在线检测技术、机器视觉等相关研究。E-mail:zhangying1984@aliyun.com

    丁红昌(1980—),男,辽宁抚顺人,博士,副教授,博士生导师,2006年、2009年和2016年于长春理工大学分别获得学士、硕士和博士学位。主要从事光学在线检测技术等相关研究。E-mail:dinghc@cust.edu.cn

    曹国华(1965—),男,黑龙江桦甸人,博士,教授,博士生导师,1988年于东北重型机械学院获得学士学位,1991年于长春光学精密机械学院获得硕士学位,2009年于长春理工大学获得博士学位,现为长春理工大学机电工程学院教授,主要从事光学在线检测技术、机器视觉等相关研究。E-mail:caogh@cust.edu.cn

  • 中图分类号: TH16

The normal alignment method for freeform surfaces based on multiple laser sensor assembly

Funds: Supported by National Key Research and Development Program of Major Scientific Instruments and Equipment’s Development (No. 2017YFF0105304); Jilin Provincial Industrial Innovation Special Fund Project (No. 2018C038-4); Jilin Province Science and Technology Development Plan Project (No. 20200401117GX)
More Information
  • 摘要: 在国产化、技术自主化的大型飞机项目上,对孔位精度的标准正逐步升高,钻头的垂直度又是这一标准中最受关注的条件。机器人自动钻铆系统工作时,所产生的装配误差、磕碰、偏移等状况,不仅降低了制造及检测的准确度,还影响了整个结构件的疲劳性能。针对这一问题,提出了基于多激光传感器装配的自适应自由曲面法线检测技术,搭建了以该方法进行姿态找正的数学模型,并研究了检测装置的标定方法及流程。同时,着重讨论了在自适应方法检测时,利用电子经纬仪等装置进行误差补偿的相关技术。该方法的验证实验结果显示,多组实验数据均达到了法向精度<±0.5°的关键技术指标要求,找正后的法线平均偏差值为0.0667°。该方法能有效补偿在制孔工作中所产生的相关误差,进一步提高机器人的定位精度及法线方向检测精度。
  • 图  1  (a)姿态找正模型;(b)使用的激光传感器型号:FASTU/CD22;(c) X'TZ'坐标平面内角θ1h1h2之间的关系

    Figure  1.  (a) Geometric model for posture alignment; (b) type of laser sensor used: FASTU/CD22; (c) relationships between θ1, h and h2 in the X'TZ' coordinate plane.

    图  2  法向标定实验所用的设备。(a)激光传感器安装示意图;(b)标定块

    Figure  2.  Equipments used in normal-direction alignment experiments. (a) Schematic diagram of the laser sensor assembly; (b) calibration blocks

    图  3  法线检测验证平台。(a)验证平台装置图;(b)实验场景图

    Figure  3.  Verification platform for detection in the normal-direction. (a) Experimental devices; (b) experimental scene

    图  4  法线找正实验设备

    Figure  4.  Experiment setup of normal-directions alignment

    图  5  转动角度为1°/次时,计算出的法线偏转角度和经纬仪测量出的偏转角度值分析

    Figure  5.  Data analysis of the Angle_cal calculated and the Angle_act measured by theodolite (rotation angle is 1°/time)

    图  6  法线检测偏差值与0.5°线进行比较

    Figure  6.  Comparison of the normal deviation data with 0.5° target deviation

    表  1  转动角度为1°/次时法线找正实验验证数据

    Table  1.   Verification data of normal-direction alignment with rotation angle of 1°/time

    实验编号传感器实时测量值/(mm)计算的法线
    变化量/(°)
    经纬仪测量的法线
    变化量/(°)
    误差标定结果
    /(°)
    h1h2h3h4
    n1_141.6431.1740.0527.17\\\
    n1_241.8130.9740.1026.800.53050.68630.1558
    n1_342.2038.9940.3826.590.72950.78090.0514
    n1_442.6031.0440.7326.480.76020.79870.0386
    n1_543.0331.0941.0326.280.78130.82690.0456
    n1_643.0131.0940.9926.300.78690.81130.0244
    n1_743.4531.1541.3226.160.85730.82220.0351
    n1_844.0531.4041.6726.050.77080.78890.0181
    n1_944.4531.5142.0625.940.78190.77720.0047
    n1_1044.9631.7042.4125.900.95390.90910.0449
    n1_1145.6332.0642.8825.940.81280.69160.1212
    下载: 导出CSV

    表  2  验证实验后所统计的法线偏差值(abs_err)数据

    Table  2.   Statistical data of normal deviation value (abs_err) in validated experiments

    实验编号2°_abs_err1°_abs_err0.1°_abs_err0.2°_abs_err
    n_20.25940.15580.02410.0025
    n_30.03140.05140.05220.0433
    n_40.02220.03860.09950.0175
    n_50.08580.04560.15920.0723
    n_60.32570.02440.02290.0136
    n_70.17470.03510.00260.0810
    n_8/0.01810.05990.0191
    n_9/0.00470.01910.0315
    n_10/0.04490.00790.0937
    n_11/0.12120.10410.0379
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-11-23
  • 修回日期:  2020-12-31
  • 网络出版日期:  2021-03-05
  • 刊出日期:  2021-04-01

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