Feature extraction and robot path planning method in 3D vision-guided welding for multi-blade wheel structures

Multi-blade wheel structures are essential components in many industrial and mechanical systems. Substituting manual welding with robot welding can significantly enhance processing efficiency for them. However, robotic welding requires complex teaching and programming to plan paths. This is not conducive to the improvement of welding efficiency. To address this issue, this paper establishes a welding robot system based on dual 3D cameras and presents a 3D vision-driven robotic path planning approach. In this method, the eye-to-hand camera and the eye-in-hand camera are used as the shooting path planning guide and the welding path planning guide, respectively. First, this paper uses a DBSCAN-based method to segment the point cloud and plan the shooting pose according to the geometric information of the blade. Second, to extract the weld accurately, this paper proposes a concave-convex feature descriptor called flatness, and feature points are extracted using a K-means-based clustering method. The weld path is obtained by fitting the feature points. Finally, to ensure welding quality stability, a welding torch posture planning method based on the neighborhood centroid is proposed, following the principle of the dihedral angle. The comparison experiment verifies that the proposed method can realize the path planning of the welding multi-blade wheel structures without teaching and programming with high efficiency and precision.