H.3.2.2. Computer vision
Shiva Zeymaran; Vali Derhami; Mehran Mehrandezh
Abstract
This paper presents an accurate and efficient method for determining the coordinates of welding seams, addressing a significant challenge in the deployment of welding robots for complex tasks. Despite welding robots’ precision in following predetermined paths, they struggle with seam identification ...
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This paper presents an accurate and efficient method for determining the coordinates of welding seams, addressing a significant challenge in the deployment of welding robots for complex tasks. Despite welding robots’ precision in following predetermined paths, they struggle with seam identification due to noisy industrial environments, stringent accuracy requirements, and computational complexity. Unlike existing approaches, which either rely on random sampling or are limited to simple geometries, our method combines splicing techniques with welding map alignment to handle complex shapes with multiple seams. This research employs a weighed method to integrate point clouds captured by RGB-D cameras, producing a low-noise point cloud. By leveraging the welding map of parts drawn, the method identifies probable regions for weld seams within the point cloud, substantially reducing the search space. This enables the system to find the weld seam in a timely manner. Knowing the approximate shape of the weld based on the available weld map, an innovative technique is then used to accurately locate the weld seam within these regions. Experimental results on fence-shaped structures in a simulated environment show a mean average error of 1.30 mm, achieving a 30% improvement in precision and a 77% reduction in computation time compared to the state-of-the-art methods. The approach's ability to accurately identify weld seams in complex shapes, coupled with its computational efficiency, suggests strong potential for real-world application. By leveraging welding maps and robust point cloud processing techniques, the method is designed to handle noise and variability, key challenges in industrial environments.
