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In the realm of industrial assembly line production, the automation of spraying operations for small- and medium-sized parts often encounters significant challenges. The primary issue lies in the inconsistent placement of different parts, complicating the automation of measurement and positioning tasks.
To address this, a sophisticated approach leveraging 2D images has been developed to assist in the preliminary positioning and classification of parts. This method involves planning the scanning and measurement path of a 3D camera based on the preliminary positioning information provided by 2D image processing. However, the cloud data points captured by the 3D camera include a large amount of irrelevant background information. To isolate accurate point cloud data for the parts, the data is segmented according to the preliminary positioning information of the parts. Point cloud registration, a critical challenge within robotics and computer vision fields, involves estimating a rigid transformation to align one point cloud to another. The final step entails registering the measured point cloud with the 3D digital model of the part to determine the exact position and orientation of the part. The 2D image is processed successively by filtering, gray equalization, binarization, morphological processing, and contour extraction. These steps effectively separate the image foreground from the background and identify the parts within the image. Recently, an innovative adaptation of the classic particle swarm optimization algorithm, enhanced with an adaptive heuristic, has been employed to tailor different schemes and scales based on specific registration conditions. This approach achieves rapid location cloud registration under strong background noise. To overcome issues of slow convergence speed and the tendency to settle into local optima, this improved algorithm incorporates learning and inertia coefficients of adaptive state registration, along with stalling coefficients and gradient descent operations for adaptive scaling. Employing a normal distribution confidence criterion minimizes the effect of fitness outliers on registration, facilitating intelligent alignment between the point cloud and the theoretical numerical model. This helps achieve precise determination of the parts' positions and orientations.
Integrating two-dimensional (2D) vision technology significantly reduces measurement times and improves efficiency through multithreaded concurrent synchronization operations. Finally, the automatic scanning of three-dimensional (3D) point clouds and the autonomous registration positioning of various parts are accomplished within 3 min, achieving an average accuracy of 2 mm. When aligning identical point clouds, the algorithm demonstrates a registration accuracy of 0.002 mm.
Despite its robustness under strong back-point cloud influence, the algorithm's registration accuracy is still greatly affected. In addition, the inherent discrepancy between the sampling consistency of the scanned point cloud and the theoretical numerical model introduces an error of about 0.5 mm, limiting further improvements in registration accuracy. Due to their limited features and small size, some aviation parts are easily misidentified and prone to significant attitude registration errors. It is necessary to further combine the advantages of 2D images and 3D point cloud data to enhance the robustness of the identification and positioning of aviation parts.
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