Sensors · 2021 · 26 citations · 18 references
EngineeringComputer ScienceManufacturing EngineeringComputer VisionIndustrial RoboticsMachine Vision3D ImagingArc WeldingPoint Cloud ProcessingRobotic WeldingWelding ProcessesWelding Robotics3D Point Cloud ProcessingGas Metal Arc WeldingLaser Displacement MeasurementLaser Displacement SensorsPoint Cloud SegmentationReal-time TrackingSeam TrackingWelding Automation
Three-dimensional (3D) zigzag-line welding seams are found extensively in the manufacturing of marine engineering equipment, heavy lifting equipment, and logistics transportation equipment. Currently, due to the large amount of calculation and poor real-time performance of 3D welding seam detection algorithms, real-time tracking of 3D zigzag-line welding seams is still a challenge especially in high-speed welding. For the abovementioned problems, we proposed a method for the extraction of the pose information of 3D zigzag-line welding seams based on laser displacement sensing and density-based clustering point cloud segmentation during robotic welding. after thee point cloud data of the 3D zigzag-line welding seams was obtained online by the laser displacement sensor, it was segmented using theρ-Approximate DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm. In the experiment, high-speed welding was performed on typical low-carbon steel 3D zigzag-line welding seams using gas metal arc welding. The results showed that when the welding velocity was 1000 mm/min, the proposed method obtained a welding seam position detection error of less than 0.35 mm, a welding seam attitude estimation error of less than two degrees, and the running time of the main algorithm was within 120 ms. Thus, the online extraction of the pose information of 3D zigzag-line welding seams was achieved and the requirements of welding seam tracking were met.
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Martin A. Fischler, Robert C. Bolles · Communications of the ACM · 1981 · 25.5K citations · Full text
A density-based algorithm for discovering clusters in large spatial Databases with Noise
Martin Ester, Hans‐Peter Kriegel, Jörg Sander et al. · 1996 · 19.1K citations
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