基于机器视觉的超视场工件识别抓取系统

Recognition and Grabbing System for Workpieces Exceeding the Visual Field Based on Machine Vision

目前,应用机器视觉系统进行工件检测时,对于工件体积相较单个相机视场范围较大的超视场工件,因无法拍摄出工件完整图片,因而存在工件识别困难以及定位不准等问题.针对这些问题,本文提出了一种基于机器视觉的超视场工件识别抓取系统WRGS(workpiece recognition and grabbing system).该系统无需拍摄工件完整图片,采用提取工件上特征点进行形状匹配的方法来实现对超视场工件的有效识别.系统定义了每类工件标准位置用于位姿计算,通过设计由粗到精抓取网络的方法,WRGS解决了超视场工件定位困难的问题,并提高了抓取精度.该系统能通过自学习来适应多种工件,从而提高了系统的柔性.通过实验例证,整个系统抓取时间小于6 s,落爪位置与目标在水平方向上的误差≤2 mm、高度误差≤0.2 mm、角度误差≤0.3°.

Currently, a machine vision system cannot take the full image of the workpiece with the size larger than the visual field of a single camera in the process of workpiece inspection. Thus, it has caused some problems such as workpiece recognition difficulty and inaccurate positioning. To solve these problems, this paper proposes an exceeding-field workpiece recognition and grabbing system(WRGS) based on machine vision. The system can realize the effective recognition of the exceeding-field workpiece by extracting the feature points on workpiece and conducting shape matching, without shooting the full image of the workpiece. The system defines the standard location of each type of workpiece for position calculation,and designs the coarse-to-fine grabbing network method to solve the positioning difficulty of exceeding-field workpieces and improve the precision of grabbing. Besides, the system can adapt to a variety of workpieces through self learning, and thus enhances the flexibility. Experiment results show that the system grabbing time is less than 6 s; the errors between the claw's position and the target in the horizontal direction, in height and angle are 2 mm or less, 0.2 mm or less, and 0.3° or less respectively.