面向零件装配的机器人工件识别

Workpiece recognition of assembly robot

针对工业机器人装配柔性低、工件识别鲁棒性差的问题,提出一种基于机器视觉的工件识别方法应用于零件装配领域。该方法首先利用双层旋转滤波器的尺寸不变性和旋转不变性实现特征点的均匀提取,利用rBRIEF算法生成带有方向信息的特征点描述符,计算Hamming距离进行特征点匹配,利用RANSAC算法对结果进行优化,实现目标工件的准确识别。用工件图片对本文方法进行检验,试验对比分析了该方法与3种传统方法在旋转、缩放变换下的工件识别效果,结果表明,该方法能够更准确、高效地识别工件。最后,搭建基于机器视觉的轴孔装配实验平台,验证工件识别的可行性。

To solve the problems of low assembly flexibility and poor workpiece recognition robustness of industrial robot, a workpiece recognition method based on machine vision is proposed for parts assembly. First, the feature points are extracted uniformly using the double-layer rotating filter with dimensional invariance and rotational invariance, then the feature point descriptors with orientation are generated by the r BRIEF algorithm, and then the Hamming distance and RANSAC algorithms are used for feature point matching and optimization to accurately identify the target artifacts. The proposed method is tested with workpiece images, and the results of workpiece recognition by the method and other three traditional methods under rotation and scaling transformation are compared and found to be more accurate and efficient in recognizing workpieces. Finally, the experimental platform of shaft-hole assembly based on machine vision is built to verify the feasibility of workpiece recognition.