芯片级原子钟核心器件识别与位姿测量

Core Devices Recognition and Pose Measurement Technology of Chip-level Atomic Clock

摘要:

为了提高芯片级原子钟的自动化装配效率与装配精度,研究了一种粗-精结合的微器件位姿检测方法。首先,分析了微器件自身的特点、应用环境,提出利用图像梯度直方图特征训练SVM分类器来实现微器件粗定位的方法,同时通过构建遮挡样本和模糊样本以解决模型在微器件离焦模糊、遮挡情况下的漏检问题,提高模型泛化能力。然后,提出利用LSD算法和直线增长方法来提取目标边缘,计算得到图像亚像素的特征点坐标。最后,通过ICP算法计算微器件在显微视场下的实时位置和姿态。实验结果表明,原子钟核心器件的识别准确率为95.3%,定位平均误差小于20μm,角度误差小于0.5°,能够满足微装配过程中微加持器准确抓取目标器件的要求。

Abstract:

To improve the efficiency and accuracy of automatic assembly of chip level atomic clock, a coarse-fine combined micro device pose detection method is proposed. Firstly, after analyzing the characteristics and application environment of the micro-devices, a rough localization method of micro devices based on SVM classifier trained by image gradient histogram features is proposed. At the same time, occlusion samples and fuzzy samples are constructed to solve the problem of missing detection in the case of defocusing and occlusion of micro devices, so as to improve the generalization ability of the model. Then, the LSD algorithm and linear growth method are used to extract the edge of the target and calculate the sub-pixel feature point coordinates of the image. Finally, the real-time position and posture of the micro device under the microscope field of view are calculated by the ICP algorithm. Experimental results show that the recognition accuracy of the core components of the device is 95.3%, the average positioning error is less than 26μm, the angle error is less than 0.5°, which can meet the needs of the robot to accurately grasp the targets in the micro assembly process.