  Near Infrared (NIR) spectroscopy is widely used in industrial quality control
and automation to test the purity and material quality of items. In this
research, we propose a novel sensorized end effector and acquisition strategy
to capture spectral signatures from objects and register them with a 3D point
cloud. Our methodology first takes a 3D scan of an object generated by a
time-of-flight depth camera and decomposes the object into a series of planned
viewpoints covering the surface. We generate motion plans for a robot
manipulator and end-effector to visit these viewpoints while maintaining a
fixed distance and surface normal to ensure maximal spectral signal quality
enabled by the spherical motion of the end-effector. By continuously acquiring
surface reflectance values as the end-effector scans the target object, the
autonomous system develops a four-dimensional model of the target object:
position in an R^3 coordinate frame, and a wavelength vector denoting the
associated spectral signature. We demonstrate this system in building
spectral-spatial object profiles of increasingly complex geometries. As a point
of comparison, we show our proposed system and spectral acquisition planning
yields more consistent signal signals than naive point scanning strategies for
capturing spectral information over complex surface geometries. Our work
represents a significant step towards high-resolution spectral-spatial sensor
fusion for automated quality assessment.
