Development and Validation of Automated Viewpoint Generation and Calibration Tools for Enhanced Robotic Surface Inspection in Aerospace Manufacturing

Abstract

Quality control is key in the advanced manufacturing of complex parts. Modern aerospace manufacturing must identify and exclude parts with visual imperfections (e.g., scratches, discolorations, dents, tool marks, etc.) to ensure safe operation. This inspection process— often manual—is not only time-consuming, but burdensome, subjective, and requires months to years of training, particularly for high-volume production operations. A reliable robotic visual inspection solution has been hindered by the small defect size, intricate part characteristics, and demand for high inspection accuracy. This work proposes a novel framework for automated inspection path planning that addresses these core features through five innovations: camera-parameter-based mesh segmentation, ray-tracing viewpoint placement, robot-agnostic viewpoint planning, Bayesian Optimization (BO) for efficient segmentation and Region Growth (RG) algorithm for adaptability to highly complex parts. The development of a viewpoint generation tool is followed by the exploration and implementation of systematic camera calibration techniques. Finally, the effectiveness of the proposed workflow is tested with simulation and experimentation on a robotic inspection of heterogeneous complex geometries.