Segmentation and location algorithm for oblong holes in robotic automatic assembly
[Objective]Oblong holes are commonly used across various industries to improve fault tolerance and adjustment capabilities.However,their complex geometric characteristics pose significant challenges for vision detection and location algorithms in industrial applications,impacting their utilization in automatic assembly processes.[Methods]This research investigates a high-precision and robust vision segmentation and location algorithm tailored for oblong holes.First,the geometric features of oblong holes,which are symmetric but lack a simple analytical description,are analyzed.This complexity renders traditional imaging methods ineffective for accurate localization.The detection and segmentation of oblong hole features are conducted using a novel vision location algorithm that integrates deep learning with conventional image processing techniques.Specifically,the algorithm employs a sequential connection framework of YOLO and fully convolutional networks to achieve accurate localization.This framework first identifies the region of interest and then performs semantic segmentation.YOLO networks rapidly detect the region of interest,prioritizing areas where the oblong hole is prominently featured.Semantic segmentation is subsequently performed using fully convolutional networks.Afterward,a skeleton feature extraction method based on medial axis transformation is applied to precisely locate the oblong hole.This method effectively reduces the impact of shape errors from semantic segmentation,achieving subpixel accuracy.However,medial axis transformation may produce redundant lines owing to the presence of image artifacts,potentially leading to inaccuracies.To address this issue,principal component analysis is employed to approximate the center of the oblong hole,thereby minimizing errors.For further precision,a Hough transformation ellipse detection method is utilized to identify the central skeleton of the oblong hole,which is interpreted both as a line segment and a special ellipse.The center of this skeleton represents the center of the oblong hole.[Results]Experimental validation conducted in a specific robotics automatic assembly system confirms the effectiveness of the proposed algorithm.The robustness of the algorithm is further demonstrated through image sampling using camera hardware distinct from that used in the training dataset.Additionally,the impact of surface features and oblong hole shapes on the detection performance is analyzed.The experimental outcomes indicate the optimal performance of the algorithm on objects with nonreflective surfaces,with minimal effect from the shape of the oblong hole on accuracy.Despite potential deformations in segmentation output due to hardware variations,the oblong hole region degenerating location algorithm,based on medial axis transformation,accurately locates the center.The final location error is recorded at 1.05 pixels,which surpasses the accuracy achieved through the direct calculation of the center of gravity of the segmented region.These results underscore the substantial benefits of the algorithm in scenarios with varying hardware and object conditions,demonstrating its high accuracy and exceptional robustness.[Conclusions]By merging deep learning techniques with traditional image processing methods,the location tasks for diverse objects are effectively resolved.The extraction of highly nonlinear features through deep learning,followed by processing with traditional image methods incorporating prior geometric knowledge,enhances the robustness and accuracy of the algorithm,making it suitable for practical production applications.
NETL
NSTL
万方数据
