手机点胶三维轨迹生成方法

刘洋; 史金龙; 钱强; 欧镇; 白素琴

doi:10.11959/j.issn.2096-6652.202407

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手机点胶三维轨迹生成方法

学术论文 | 更新时间：2024-06-05

- 手机点胶三维轨迹生成方法
- Three-dimensional trajectory generation method for mobile phone dispensing
- 智能科学与技术学报 2024年6卷第1期页码：100-110
- 作者机构：
  
  江苏科技大学计算机学院，江苏镇江 212000
- 作者简介：
  
  [ "刘洋（2000- ），男，江苏科技大学计算机学院硕士生，主要研究方向为计算机视觉、三维重建。" ]
  [ "史金龙（1976- ），男，博士，江苏科技大学计算机学院教授，主要研究方向为计算机视觉、深度学习。" ]
  [ "钱强（1978- ），男，江苏科技大学计算机学院讲师，主要研究方向为机器学习、深度学习。" ]
  [ "欧镇（1973- ），男，江苏科技大学计算机学院讲师，主要研究方向为信息系统建模、系统架构设计和移动端应用。" ]
  [ "白素琴（1976- ），女，江苏科技大学计算机学院副教授，主要研究方向为计算机视觉。" ]
- 基金信息：
  
  国家自然科学基金项目(51875270);民航智慧机场理论与系统重点实验室开放基金(SATS202207)
- DOI：10.11959/j.issn.2096-6652.202407
  中图分类号： TP391
- 收稿：2023-12-25，
  
  修回：2024-03-02，
  
  纸质出版：2024-03-15
- 稿件说明：
移动端阅览
刘洋, 史金龙, 钱强, 等. 手机点胶三维轨迹生成方法[J]. 智能科学与技术学报, 2024,6(1):100-110.

LIU Yang, SHI Jinlong, QIAN Qiang, et al. Three-dimensional trajectory generation method for mobile phone dispensing[J]. Chinese Journal of Intelligent Science and Technology, 2024, 6(1): 100-110.
刘洋, 史金龙, 钱强, 等. 手机点胶三维轨迹生成方法[J]. 智能科学与技术学报, 2024,6(1):100-110. DOI： 10.11959/j.issn.2096-6652.202407.

LIU Yang, SHI Jinlong, QIAN Qiang, et al. Three-dimensional trajectory generation method for mobile phone dispensing[J]. Chinese Journal of Intelligent Science and Technology, 2024, 6(1): 100-110. DOI： 10.11959/j.issn.2096-6652.202407.

摘要

智能手机边框在制造过程中往往存在形变问题，先前的视觉定位方法难以得到精确的边框点胶位置。针对此问题，提出了一种新颖的手机点胶三维轨迹生成方法。首先，使用三维线激光测量仪扫描边框，引入基于位姿图的点云拼接算法解决边框点云的拼接问题。采用基于模板的方法，标注模板点胶轨迹。然后，通过基于变形图的非刚性配准算法将模板与目标边框对齐，得到变换后的轨迹，并提出轨迹优化策略生成精准的目标点胶轨迹。实验证明，所提方法生成的轨迹精度可达0.01 mm以下，且速度可达到在线实时测量效果。

Abstract

Due to the potential issue of non-rigid deformations in the manufacturing process of mobile phone frames

most existing dispensing automation methods struggled to acquire accurate dispensing positions. For this purpose

an innovative approach of dispensing trajectory generation was proposed

which addressed the limitations associated with previous methods. Firstly

the phone frames were scanned using a 3D laser profiler

then a point cloud registration algorithm based on the pose graph was introduced to reconstruct the point clouds of frames. The template-based approach was employed

and the dispensing trajectory for template was manually annotated. Subsequently

the template was aligned with the target by a non-rigid registration algorithm based on the deformation graph

thereby the transformed trajectory was obtained. Furthermore

a trajectory refinement strategy was presented to generate the robust

accurate dispensing trajectory of the target. Extensive experiments demonstrate that the errors of the generated dispensing trajectories were consistently below 0.01 mm

and the speed can meet the real-time requirement of practical applications.

关键词

Keywords

references

程方, 张曦, 张金松. 基于机器视觉的点胶机定位系统[J]. 机械设计与制造, 2013(3): 101-104.

CHENG F, ZHANG X, ZHANG J S. A positioning system for dispensing machine based on machine-vision[J]. Machinery Design ＆Manufacture, 2013(3): 101-104.

张胜奎. 智能点胶机关键技术研究[D]. 焦作: 河南理工大学, 2011.

ZHANG S K. Study on key technologies of intelligent dispensing system[D]. Jiaozuo: Henan Polytechnic University, 2011.

李顺君, 钱强, 史金龙, 等. 基于卷积神经网络的扁桃体咽拭子采样机器人[J].计算机工程与应用, 2022, 58(15): 324-329.

LI S J, QIAN Q, SHI J L, et al. Sampling robot for tonsil swabs based on convolutional neural network[J]. Computer Engineering and Applications, 2022, 58(15): 324-329.

吴佳毅, 陈建强, 邓宇平, 等.基于机器视觉检测的手机显示屏点胶系统[J].自动化与信息工程, 2019, 40(3): 43-48.

WU J Y, CHEN J Q, DENG Y P, et al. Dispensing system of mobile screen based on machine visionb inspection[J]. Automation & Information Engineering, 2019, 40(3): 43-48.

贺昌兵. 基于三维视觉的点胶机工件定位与路径提取技术研究[D]. 成都: 电子科技大学, 2023.

HE C B. Research on workpiece positioning and path extraction technology of dispensing machine based on three-dimensional vision[D]. Chengdu: University of Electronic Science and Technology of China, 2023.

陈鸿雨. 面向曲面中框的3D视觉引导点胶方法及系统研究[D]. 哈尔滨: 哈尔滨理工大学, 2023.

CHEN H Y. 3D visual guidance dispensing for curved middle frame method and system research[D]. Harbin: Harbin University of Science and Technology, 2023.

YOU B, CHEN H Y, LI J Y, et al. Fast point cloud registration algorithm based on 3DNPFH descriptor[J]. Photonics, 2022, 9(6): 414.

CHEN Y, MEDIONI G. Object modelling by registration of multiple range images[J]. Image and Vision Computing, 1992, 10(3): 145-155.

CHOI S, ZHOU Q Y, KOLTUN V. Robust reconstruction of indoor scenes[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2015: 5556-5565.

LU F, MILIOS E. Globally consistent range scan alignment for environment mapping[J]. Autonomous robots, 1997, 4: 333-349.

MORÉ J J. The Levenberg-Marquardt algorithm: implementation and theory[M]//WATSON G A, ed. Lecture Notes in Mathematics. Heidelberg: Springer, 1978: 105-116.

ESTER M, KRIEGEL H P, SANDER J, et al. A density-based algorithm for discovering clusters in large spatial databases with noise[C]//Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. New York: ACM, 1996: 226-231.

LEE D T, SCHACHTER B J. Two algorithms for constructing a Delaunay triangulation[J]. International Journal of Computer & Information Sciences, 1980, 9(3): 219-242.

KIMMEL R, SETHIAN J A. Computing geodesic paths on manifolds[J]. Proceedings of the National Academy of Sciences, 1998, 95(15): 8431-8435.

HOLLAND P W, WELSCH R E. Robust regression using iteratively reweighted least-squares[J]. Communications in Statistics-theory and Methods, 1977, 6(9): 813-827.

YAO Y X, DENG B L, XU W W, et al. Quasi-newton solver for robust non-rigid registration[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE Press, 2020: 7597-7606.

LANGE K. Optimization[M]. Berlin: Springer Science & Business Media, 2013.

NOCEDAL J. Updating quasi-Newton matrices with limited storage[J]. Mathematics of Computation, 1980, 35(151): 773-782.

BESL P J, MCKAY N D. Method for registration of 3-D shapes[C]//Sensor Fusion Iv: Control Paradigms and Data Structures. [S.l.:s.n.], 1992: 586-606.

ZHOU Q Y, PARK J, KOLTUN V. Fast global registration[M]//Computer Vision - ECCV 2016. Cham: Springer International Publishing, 2016: 766-782.

AMBERG B, ROMDHANI S, VETTER T. Optimal step nonrigid ICP algorithms for surface registration[C]//Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2007: 1-8.

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