面向批量精密装配的显微特征定位

Microscopic feature localization for mass precision assembly tasks

基于显微机器视觉的特征定位是精密装配中重要的一环,批量精密装配中装配状态不同导致特征定位错误,使流程中断进而影响装配效率,因此需要建立强鲁棒性的特征定位算法。提出一种融合方向梯度直方图特征和局部二值模式特征的支持向量机模型,并采用金字塔搜索策略提高识别效率,实现显微特征定位。在自行研制的精密自动装配设备上进行性能测试,采集不同特征进行支持向量机的训练,研究了纹理和光照等干扰因素对定位稳定性和精度的影响,并进行定位精度实验及某组件批量装配。实验结果表明:利用本方法提取目标特征位置,在多种条件下均具有良好的单峰性和重复精度,识别准确率达到98%,定位精度优于4μm,装配精度优于7μm。本方法能够满足实际批量生产中不同装配条件下的定位需求,为自动化精密装配定位提供了有效的解决方案。

Feature localization based on microscopic vision is important for precision assembly. Because assembly states vary in a batch assembly,feature positioning errors often arise,which significantly interrupt the process and affect efficiency. Therefore,establishing a solid and robust feature localization algorithm is crucial. This paper proposes a support vector machine(SVM)model for synthesizing gradient histograms and local binary patterns. Furthermore,the pyramid search strategy is employed to improve the recognition efficiency and realize the micro-feature localization method. Performance verification and heuristic application are conducted on self-developed precision automatic assembly equipment,and different features are collected for SVM training. The influences of interference factors such as texture and illumination on the positioning stability are investigated in detail. Additional experiments regarding the positioning accuracy and actuator component assembly are performed. Under various conditions,the proposed approach presents good unimodal,repetitive accuracy and robustness. A recognition accuracy rate of 98% can be achieved. The positioning accuracy is better than 4 μm,and the actual assembly accuracy is better than 7 μm. The feature localization method can meet the localization requirements under different assembly conditions in real batch production and provides an effective solution for precision automatic assembly localization.