Visual composite positioning for precision microassembly

WANG Xiaodong; CUI Shipeng; XU Zheng; LU Shiqin

doi:10.37188/OPE.20233119.2857

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Visual composite positioning for precision microassembly

Micro/Nano Technology and Fine Mechanics | 更新时间：2023-10-12

- Visual composite positioning for precision microassembly
- Optics and Precision Engineering Vol. 31, Issue 19, Pages: 2857-2866(2023)
- 作者机构：
  
  大连理工大学机械工程学院，辽宁大连 116023
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20233119.2857
  CLC： TH161.7;TP391.41
- Received：23 March 2023，
  
  Revised：16 May 2023，
  
  Published：10 October 2023
- 稿件说明：
移动端阅览
王晓东,崔世鹏,徐征等.面向精密微装配的视觉复合定位[J].光学精密工程,2023,31(19):2857-2866.

WANG Xiaodong,CUI Shipeng,XU Zheng,et al.Visual composite positioning for precision microassembly[J].Optics and Precision Engineering,2023,31(19):2857-2866.
王晓东,崔世鹏,徐征等.面向精密微装配的视觉复合定位[J].光学精密工程,2023,31(19):2857-2866. DOI： 10.37188/OPE.20233119.2857.

WANG Xiaodong,CUI Shipeng,XU Zheng,et al.Visual composite positioning for precision microassembly[J].Optics and Precision Engineering,2023,31(19):2857-2866. DOI： 10.37188/OPE.20233119.2857.

摘要

基于机器视觉的微小特征定位是精密自动化装配的关键环节，外界干扰和零件本身差异等容易引起视觉引导错误，影响装配成功率，因此提出一种由粗定位与精定位两步组成的复合定位方法。首先通过基于卷积神经网络的目标框检测算法提取感兴趣区域实现粗定位，在此基础上通过轮廓几何特征配准的方式实现零件精定位，算法中还采用自动标注辅助的动态学习机制解决不同批次零件间差异导致定位失败率较高的问题。在自研的装配设备上对该方法进行测试，分析了亮度、离焦和位姿变化对视觉定位算法鲁棒性的影响，并进行了定位精度及小批量装配实验测试。结果表明：本文方法在多种干扰下的装配成功率达到97%，视觉定位的绝对精度与重复精度均优于2 μm，装配精度优于10 μm，能够满足精密微装配对定位算法精度与稳定性的要求。

Abstract

Microfeature positioning based on machine vision is a crucial aspect of precision automated assembly. External interference and differences in the parts themselves can easily cause visual guidance errors and the success rate of assembly. Therefore， a composite positioning method consisting of rough positioning and fine positioning was proposed. First， the region of interest was extracted through a target-frame-detection algorithm based on a convolutional neural network to achieve rough positioning. Based on this， precise positioning of parts was achieved through contour geometric feature registration. A dynamic learning mechanism assisted by automatic labeling was also adopted in the algorithm to solve the problem of the high positioning failure rate resulting from the difference between the different batches of parts. The method was tested on assembly equipment developed by the research group. The effects of brightness， defocusing， and posture changes on the robustness of visual positioning algorithms were analyzed. Furthermore， positioning accuracy and small-batch assembly experiments were conducted. The results show that the proposed method has good robustness and repeatability with various forms of interference， with an assembly success rate of 97%. Both the absolute accuracy and repetitive accuracy of visual positioning are<2 μm， and assembly accuracy is<10 μm. Therefore， the research results effectively meet the dual requirements of both accuracy and robustness of the positioning algorithm in precision microassembly.

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