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基于三维点云的磁极焊缝识别及机器人轨迹生成技术

贾瑞燕, 李海超, 魏方锴, 徐勇, 周宇飞

贾瑞燕, 李海超, 魏方锴, 徐勇, 周宇飞. 基于三维点云的磁极焊缝识别及机器人轨迹生成技术[J]. 焊接学报, 2024, 45(11): 50-54. DOI: 10.12073/j.hjxb.20240719002
引用本文: 贾瑞燕, 李海超, 魏方锴, 徐勇, 周宇飞. 基于三维点云的磁极焊缝识别及机器人轨迹生成技术[J]. 焊接学报, 2024, 45(11): 50-54. DOI: 10.12073/j.hjxb.20240719002
JIA Ruiyan, LI Haichao, WEI Fangkai, XU Yong, ZHOU Yufei. Magnetic pole weld identification and robot trajectory generation technology based on 3D point cloud[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(11): 50-54. DOI: 10.12073/j.hjxb.20240719002
Citation: JIA Ruiyan, LI Haichao, WEI Fangkai, XU Yong, ZHOU Yufei. Magnetic pole weld identification and robot trajectory generation technology based on 3D point cloud[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(11): 50-54. DOI: 10.12073/j.hjxb.20240719002

基于三维点云的磁极焊缝识别及机器人轨迹生成技术

基金项目: 黑龙江省重点研发计划项目(GA21A401)
详细信息
    作者简介:

    贾瑞燕,硕士,高级工程师;主要从事水火电发电机组关键部件焊接技术研究,高效焊接工艺及设备智能化制造技术开发与应用; Email: jry@hec-china.com

    通讯作者:

    李海超,博士,副教授;Email: lihaichao@hit.edu.cn.

  • 中图分类号: TG 409

Magnetic pole weld identification and robot trajectory generation technology based on 3D point cloud

  • 摘要:

    针对大型水电站发电机磁极变长度、变间隙的复杂焊缝存在的示教编程效率低、精度差的问题,开发了一种基于光栅视觉传感的焊缝识别及机器人轨迹免示教生成技术. 采用安装于机器人末端的光栅传感器获取不同部位的磁极焊缝点云,提出了一种结合机器人工具位姿变换矩阵和迭代最近点算法(ICP)的点云配准算法,得到大尺寸磁极焊缝完整点云数据. 基于随机采样一致性(RANSAC)开发了焊缝识别算法,实现了机器人焊接轨迹的自动生成. 结果表明,该算法可识别出多种复杂工况的磁极焊缝,识别率高,抗干扰能力强,平均识别误差在±0.4 mm范围内,满足焊接要求.

    Abstract:

    Aiming at the problems of low efficiency and poor accuracy of teaching programming in complex welds with variable magnetic pole length and gap of large hydropower generators, a technology of welding seam identification and robot track generation without teaching was developed based on grating visual sensing. A grating sensor installed at the end of the robot was used to obtain the point cloud of the magnetic pole weld at different positions. A point cloud registration algorithm combining the robot tool pose transformation matrix and iterative closest point algorithm (ICP) was proposed to obtain the complete point cloud data of the large size magnetic pole weld. Based on random sampling consistency (RANSAC), a weld recognition algorithm was developed to realize the automatic generation of robot welding trajectories. The results show that the algorithm can identify a variety of complex magnetic pole welds with high recognition rate and strong anti-interference ability, and the average recognition error is with in ± 0.4 mm, which meets the welding requirements.

  • 图  1   焊接试验平台

    Figure  1.   Welding test platform

    图  2   系统上位机软件界面

    Figure  2.   PC system interface

    图  3   磁极焊缝结构示意图

    Figure  3.   Schematic of pole joints

    图  4   磁极焊缝识别算法流程图

    Figure  4.   Visual recognition system technology flow chart

    图  5   点云配准图

    Figure  5.   PC Point cloud registration

    图  6   焊缝位置识别

    Figure  6.   Weld position identification

    图  7   完整焊缝点云图

    Figure  7.   Complete weld point cloud

    图  8   焊缝整体成形

    Figure  8.   Weld forming

    图  9   x,y,z轴的识别误差

    Figure  9.   Recognition errors of x,y and z

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    其他类型引用(3)

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出版历程
  • 收稿日期:  2024-07-18
  • 网络出版日期:  2024-09-25
  • 刊出日期:  2024-11-24

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