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Subsurface Prospecting by Planetary Drones

Award Information
Agency: National Aeronautics and Space Administration
Branch: N/A
Contract: NNX15CK15P
Agency Tracking Number: 150119
Amount: $124,943.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: T4.02
Solicitation Number: N/A
Timeline
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
 Kerry Snyder
 CTO
 (814) 577-7452
 kerry.snyder@astrobotic.com
Business Contact
 Steven Huber
Title: Business Official
Phone: (281) 389-8171
Email: steven.huber@astrobotic.com
Research Institution
 Carnegie Mellon University
 Robert Kearns
 
5000 Forbes Ave
Pittsburgh, PA 15213-3815
United States

 (412) 268-5837
 Domestic Nonprofit Research Organization
Abstract

The proposed program innovates subsurface prospecting by planetary drones to seek a solution to the difficulty of robotic prospecting, sample acquisition, and sample characterization at multiple hazardous locations in a single mission. Innovation focuses on a specific, challenging scenario: sub-surface access of multiple lava tubes by drones far enough from Earth for speed-of-light latency to preclude direct human control. The technology will be broadly applicable to resource prospecting in cold traps, dark craters, cryovolcanoes, asteroids, comets, and other planets. The technology is also applicable to Earth-relevant problems such as the detection of poisonous and explosive gases and flammable dust in mines; and surveying urban canyons; exploring bunkers and caves.

The proposed innovation is the development of Anytime Motion Planners that can generate feasible guidance routines to accomplish subsurface prospecting by planetary drones. Anytime Motion Planners are algorithms that can quickly identify an initial feasible plan, then, given more computation time available during plan execution, improve the plan toward an optimal solution. In addition to Anytime Motion Planners, optimal guidance routines will also be innovated in this work by formulating the Generic Autonomous Guidance Optimal Control Problem (Problem G&C) (Pavone, Acikmese, Nesnas, & Starek, 2013) as a convex optimization problem and employing interior-point methods to solve the resulting problem to global optimality. This work will determine whether optimal solutions may be computed quickly enough to be useful in practice.

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

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