  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
