Long-Range Terrain Characterization for Productive Regolith Excavation

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.

Regolith excavation is a fundamental need of government and commercial endeavors on the Moon and Mars in establishing habitats, landing zones, observatories, roads and resource utilization facilities. The specific proposed technologies will enhance prospecting and excavating missions by enabling better prediction of subsurface volatiles to determine the regions of greatest value for sample acquisition and excavation. This has the potential to enhance near term missions like Resource Prospector Mission and Mars 2020 and follow-ons that may include sample return or site preparation and in-situ resource utilization for a lunar or Martian base.

Development of terrain characterization technology for excavation robots will lead to commercialization opportunities in earthworking equipment. In terrestrial construction, excavation machines must still detect buried hazards and the traversability of soil. Sensing the physical characteristics of both the surface and the subsurface at long-range as in this research will increase the reliability, safety, and efficiency of autonomous terrestrial excavators. Reliable, long-range detection of loose terrain hazards will also lead to commercialization opportunities in military, search-and-rescue, agricultural, and consumer vehicles. In all cases, vehicles would benefit from safeguarding in the presence of non-geometric hazards in off-road situations. Astrobotic could package and sell the technology to vehicle manufactures for inclusion in ground vehicle development.

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