Purpose This paper aims to present an automated programming system for the painting of structured industrial workpieces. The system aims to address the challenges posed by multisource uncertainties, such as unknown workpiece models or deviations in digital models during the programming of conventional painting robots. Design/methodology/approach This work combines high-precision 3D point cloud sensing devices with deep learning technology to enhance the system’s adaptability. Surface geometries are captured via line-structured light scanning, followed by targeted denoising and preprocessing. A hybrid segmentation approach, incorporating geometric feature learning and PointNet++-based instance segmentation, is used to classify the point clouds into four distinct surface types. In addition, hierarchical trajectory planning methods are developed for efficient painting. Findings Simulations and experiments demonstrate that the average deviation of paint distribution is less than 7%, with a coverage rate exceeding 99%. These results confirm the system’s effectiveness and practicality in improving paint quality under challenging environments. Originality/value This study proposes an automatic robotic painting system that significantly enhances adaptability for industrial workpieces with model uncertainties, offering a solution with real-time perception-to-action capability. The deep learning segmentation and trajectory planning methods introduced in this work can be extended to other industrial robotic applications requiring adaptive processing of unmodeled geometries.
He et al. (Mon,) studied this question.