Region-based robotic machining is considered an effective strategy for automatically repairing paint film defects compared to conventional global machining. However, this process faces challenges due to irregularities in defect position, shape, and size. To overcome these challenges, this paper proposes a model-enabled robotic machining framework for repairing paint film defects by leveraging the workpiece model as an enabling means. Within the system framework, an improved YOLOv5 algorithm is presented at first to enhance the visual detection accuracy of paint film defects in terms of network structure and loss function. Additionally, a target positioning method based on the pixel-point inverse projection technology is developed to map the 2D defect detection results onto the workpiece 3D model, which primarily aims at obtaining the orientation information through the connection between the monocular vision unit and the model. Finally, an optimal tool deployment strategy by virtue of the least projection coverage circle is proposed to determine the least machined position as well as the shortest robot path by constructing the mapping between the defects and the tool operation size. The constructed system framework is verified effective and practical by the experiments of region-based robotic grinding and repairing of paint film defects on high-speed train (HST) body sidewalls.

中文翻译：

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用于修复漆膜缺陷的基于模型的机器人加工框架


与传统的全局加工相比，基于区域的机器人加工被认为是自动修复漆膜缺陷的有效策略。然而，由于缺陷位置、形状和尺寸的不规则性，该过程面临挑战。为了克服这些挑战，本文提出了一种基于模型的机器人加工框架，通过利用工件模型作为实现手段来修复漆膜缺陷。在系统框架内，首先提出了改进的YOLOv5算法，从网络结构和损失函数方面提高了漆膜缺陷视觉检测的精度。此外，还开发了基于像素点逆投影技术的目标定位方法，将2D缺陷检测结果映射到工件3D模型上，主要目的是通过单目视觉单元与模型之间的连接获得方位信息。最后，提出了一种基于最小投影覆盖圆的最优刀具部署策略，通过构建缺陷与刀具操作尺寸之间的映射来确定最少加工位置以及最短机器人路径。通过高速列车车身侧壁漆膜缺陷区域机器人打磨修复实验，验证了所构建的系统框架的有效性和实用性。