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Long-Range Terrain Characterization for Productive Regolith Excavation

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: NNX15CA63P
Agency Tracking Number: 150120
Amount: $124,943.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: T4.02
Solicitation Number: N/A
Solicitation Year: 2015
Award Year: 2015
Award Start Date (Proposal Award Date): 2015-06-17
Award End Date (Contract End Date): 2016-06-17
Small Business Information
2515 Liberty Ave
Pittsburgh, PA 15222-4613
United States
DUNS: 019738852
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 William Whittaker
 University Professor
 (412) 268-1338
Business Contact
 Steven Huber
Title: Business Official
Phone: (281) 389-8171
Research Institution
 Carnegie Mellon University
 Robert Kearns
5000 Forbes Ave
Pittsburgh, PA 15213-3815
United States

 (412) 268-5837
 Domestic Nonprofit Research Organization

The proposed research will develop long-range terrain characterization technologies for autonomous excavation in planetary environments. This work will develop a machine learning framework for long-range prediction of both surface and subsurface terrain characteristics that: (1) indicate the excavation-value of the material and (2) describe how hazardous terrain is to a robotic excavator. Factors influencing importance include the mineral composition of the material and the presence and concentration of volatiles. Terrain hazards will include loose terrain that could cause wheels to sink or slip as well as the presence of surface and subsurface rocks that would inhibit excavation.

This work will develop technologies for long-range terrain mechanical characterization and volatile prediction with high spatial coverage. Ground penetrating radars and neutron spectrometers provide reasonable accurate estimates of subsurface composition and volatile accumulation; however, they are limited in sampling range and area. Cameras and LIDAR will instead be used to measure reflected radiation, temperature, and geometry at long range with a wide field of view. From these measurements, the thermal properties and spectral reflectance curves of the terrain will be estimated, since both are correlated to terrain composition and traversability. These properties, along with geometry, will be fed into a machine learning framework for prediction of terrain characteristics. Priors will be generated based on data from orbital satellites. Measurements of material composition, volatile accumulation, and traversability will be generated from expert labeling, neutron spectrometers, and wheel slip measurements, respectively. These measurements will be used to train machine learning algorithms for long-range prediction of terrain mechanical characteristics and resource concentration.

* Information listed above is at the time of submission. *

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