  Finishing operations on large-scale composite components like wind turbine
blades, including trimming and sanding, often require multiple workers and part
repositioning. In the composites manufacturing industry, automation of such
processes is challenging, as manufactured part geometry may be inconsistent and
task completion is based on human judgment and experience. Implementing a
mobile, collaborative robotic system capable of performing finishing tasks in
dynamic and uncertain environments would improve quality and lower
manufacturing costs. To complete the given tasks, the collaborative robotic
team must properly understand the environment and detect irregularities in the
manufactured parts. In this paper, we describe the initial implementation and
demonstration of a polarized computational imaging system to identify defects
in composite laminates. As the polarimetric images are highly relevant to the
surface micro-geometry, they can be used to detect surface defects that are not
visible in conventional color images. The proposed vision system successfully
identifies defect types and surface characteristics (e.g., pinholes, voids,
scratches, resin flash) for different glass fiber and carbon fiber laminates.
