基于改进 Blob 分析的机器人批量搬运纠偏研究

为了解决工业机器人在快节拍搬运任务中因为机械定位误差导致无法正确放置产品的问题,提出了一种基于改进Blob分析的两点纠偏定位的两步法策略。第1步基于机器视觉与图像处理技术利用改进的Blob分析方法识别出定位点,第2步利用平面仿射变换原理快速定位大量产品的位置。其中改进的Blob分析方法基于图像灰度反转、直方图均衡化、图像滤波技术结合阈值分水岭分割算法实现了对有限光照条件下不易成像的黑色托盘图像中定位圆形特征的可靠分割与圆心坐标提取。从而第2步的两点定位的托盘定位策略根据可靠的托盘圆心位置结果来计算托盘偏移前后的平面仿射变换矩阵以完成托盘的纠偏功能。最后通过在实际生产线的搬运工站实验证明基于改进Blob分析的两点纠偏定位的两步法策略的托盘识别准确性和召回率分别为99.75%和99.75%,同时产品的搬运时间降低了20.52%,提高了机器人下料系统的可靠性和效率。

Research on correction of robot batch handling based on theimproved Blob analysis

To solve the problem that industrial robots cannot place products correctly due to mechanical positioning error in fast beathandling task, this article proposes a two-step strategy for two-point corrective positioning based on the improved Blob analysis. The firststep identifies the positioning points based on machine vision and image processing techniques using the improved Blob analysis method.The second step quickly locates the position of a large number of products using the principle of planar affine transformation. Theimproved Blob analysis method is based on image grayscale inversion, histogram equalization, and image filtering techniques combinedwith threshold watershed segmentation algorithms to achieve reliable segmentation and circle center coordinate extraction for locatingcircular features in black pallet images that are not easily imaged under limited lighting conditions. Therefore, the two-point positioningstrategy could achieve the planar affine transformation matrix before and after the pallet offset based on the reliable pallet circle centerposition results to complete the pallet offset correction function. Finally, the experiments at the handling station of the actual productionline demonstrate that the accuracy and recall rate of the two-step strategy of two-point deflection correction positioning based on theimproved Blob analysis are 99. 75% and 99. 75% , respectively, while the product handling time is reduced by 20. 52% . The reliabilityand efficiency of the robot unloading system are improved.