Visual inspection is a crucial yet time-consuming task across various industries. Numerous established methods employ machine learning in inspection tasks, necessitating specific training data that includes predefined inspection poses and training images essential for the training of models. The acquisition of such data and their integration into an inspection framework is challenging due to the variety in objects and scenes involved and due to additional bottlenecks caused by the manual collection of training data by humans, thereby hindering the automation of visual inspection across diverse domains. This work proposes a solution for automatic path planning using a single depth camera mounted on a robot manipulator. Point clouds obtained from the depth images are processed and filtered to extract object profiles and transformed to inspection target paths for the robot end-effector. The approach relies on the geometry of the object and generates an inspection path that follows the shape normal to the surface. Depending on the object size and shape, inspection paths can be defined as single or multi-path plans. Results are demonstrated in both simulated and real-world environments, yielding promising inspection paths for objects with varying sizes and shapes. Code and video are open-source available at: https://github.com/CuriousLad1000/Auto-Path-Planner

翻译：视觉检测是各行业中一项关键但耗时的任务。众多已有方法在检测任务中采用机器学习，这需要特定的训练数据，包括预定义的检测位姿和模型训练所必需的训练图像。由于涉及的物体和场景多样性，以及人工收集训练数据造成的额外瓶颈，获取此类数据并将其整合到检测框架中颇具挑战性，从而阻碍了视觉检测在多个领域的自动化。本文提出了一种利用安装在机器人机械臂上的单个深度相机进行自动路径规划的解决方案。对从深度图像获取的点云进行处理和滤波，以提取物体轮廓，并将其转化为机器人末端执行器的检测目标路径。该方法依赖于物体的几何形状，生成沿表面法线方向的检测路径。根据物体的大小和形状，检测路径可定义为单路径或多路径规划。在仿真和真实环境中均展示了实验结果，对具有不同大小和形状的物体生成了具有前景的检测路径。代码和视频已开源，地址为：https://github.com/CuriousLad1000/Auto-Path-Planner