  In this paper, we present a new robotic system to perform defect inspection
tasks over free-form specular surfaces. The autonomous procedure is achieved by
a six-DOF manipulator, equipped with a line scan camera and a high-intensity
lighting system. Our method first uses the object's CAD mesh model to implement
a K-means unsupervised learning algorithm that segments the object's surface
into areas with similar curvature. Then, the scanning path is computed by using
an adaptive algorithm that adjusts the camera's ROI to observe regions with
irregular shapes properly. A novel iterative closest point-based projection
registration method that robustly localizes the object in the robot's
coordinate frame system is proposed to deal with the blind spot problem of
specular objects captured by depth sensors. Finally, an image processing
pipeline automatically detects surface defects in the captured high-resolution
images. A detailed experimental study with a vision-guided robotic scanning
system is reported to validate the proposed methodology.

Robotics