Vision-Guided Robotic Scraping for Irregular Cabin Sections with Adaptive Trajectory Generation

The bonding between cabin sections and exterior shells represents a critical manufacturing operation in shell assembly, directly determining the reliability and structural performance of the assembled structure. However, traditional manual scraping suffers from low efficiency, poor consistency, and heavy reliance on manual operation, while conventional teach-and-repeat robotic automation fails to adapt to significant manufacturing tolerances and complex surface curvatures common in large-scale shell components. To address these challenges, this paper proposes a vision-guided robotic scraping method that generates adaptive trajectories on irregular cabin sections. The method achieves full pipeline integration and is particularly suited for production lines where various models share similar macro-geometries but possess subtle geometric variations. A system integrating a laser profile sensor is developed to perceive surface geometry and local normal vectors. By establishing a unified coordinate transformation chain and a scan–mesh–spline workflow, the sensed geometric information is directly mapped to the robot end-effector pose. A trajectory generation algorithm based on point cloud meshing and B-spline interpolation is employed to construct continuous, smooth scraping paths that accommodate geometric deviations without relying on complex fixtures. Unlike RGB-D correction-based methods that require pre-programmed initial trajectories, or CAD-driven offline programming that cannot adapt to manufacturing deviations, the proposed approach directly generates conformal scraping paths from measured geometry. Experimental results on a typical cabin section demonstrate that the generated trajectories accurately follow the surface normals, achieving a low standard deviation of 36 μm in adhesive layer thickness, indicating excellent thickness consistency and uniformity. Furthermore, the automated process reduced the total operation time to approximately 40 min, improving production efficiency by more than two times compared to manual operations, thereby validating the robustness and suitability of the method for high-precision batch manufacturing.