In response to the challenges about the low efficiency of welding in the aerospace field

particularly the complex intersection line welding

this paper proposes a teaching-free autonomous welding method for robots based on an optimized RANSAC and ICP point cloud algorithm for intersecting lines. First

based on the intersecting line theoretical model

the point cloud data of the collected workpiece is preprocessed. The optimized RANSAC algorithm is then used to select a set number of points as sample points to fit a surface

calculating the distance from all data points to the cylinder. Points that fall on the fitted surface are selected according to a preset threshold

and a new cylindrical surface equation is fitted using the selected points. Next

the ICP algorithm iteratively searches for the closest point pairs between two point clouds

calculating the optimal rigid transformation to align the source point cloud with the target point cloud in the target coordinate system. Finally

experimental results validate the proposed teaching-free autonomous welding method for complex spatial curves

using the optimized RANSAC and ICP point cloud optimization algorithms.