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 ![PROSPECT](media/prospect_header.png)
 
 ## Abstract
-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 na\"{\i}ve 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.
+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<sup>3</sup>** 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 naïve 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.
 
 
 - *†Equal Contribution; \*Corresponding author*
 - *<sup>1</sup>Institute for Experiential Robotics*
 - *<sup>2</sup>Electrical and Computer Engineering Department, Northeastern University, Boston, Massachusetts, USA.*
 - *<sup>3</sup>Robotics Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.*
-- *<sup>4</sup>Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, Massachusetts, USA.*
+- *<sup>4</sup>Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, Massachusetts, USA.*