3D Vision-Guided Adaptive 3D Ultrasonic Scanning for Robotic Arms: Nondestructive Testing of Aerospace Components

In view of the bottleneck problems existing in the 3D ultrasonic testing of aircraft composite laminated structures-including heavy reliance on manual operation, resulting in low detection efficiency, and the inability of traditional robotic arms to adapt to the testing of complex curved surfaces due to their dependence on predefined fixed trajectories-this paper proposes an automated 3D ultrasonic testing method based on 3D vision guidance for robotic arms. Firstly, the proposed Yolo-Mask model is adopted to realize the visual recognition and segmentation of composite component regions, after which the segmentation results are mapped to the depth map and further converted into the surface point cloud of the material. Secondly, on the basis of point cloud preprocessing and trajectory point extraction, the automatic planning of the robotic arm's scanning trajectory is achieved, which drives the robotic arm to perform precise motion and to synchronously collect spatial pose and ultrasonic testing data. Finally, 3D reconstruction is completed via a fusion algorithm, and 3D images of the material's internal structures are generated. Experimental verification shows that the proposed method achieves a Segm-mAP of 97.4%, a detection speed of 11.7 fps, and a 3D imaging error of less than 0.1 mm, thereby realizing fully automated detection throughout the entire process. This research provides an effective solution for the non-destructive testing of aircraft composite structures.