Advanced Verification Toolset for Learning-Based UAS Operating in Uncertain Environments

Award Information
Agency:
Department of Defense
Branch
Air Force
Amount:
$149,985.00
Award Year:
2014
Program:
SBIR
Phase:
Phase I
Contract:
FA8650-14-M-2454
Agency Tracking Number:
O133-HS2-1038
Solicitation Year:
2013
Solicitation Topic Code:
OSD13-HS2
Solicitation Number:
2013.3
Small Business Information
Aurora Flight Sciences Corporation
9950 Wakeman Drive, Manassas, VA, 20110-
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
604717165
Principal Investigator:
Sacin Jain
Sr. GNC Engineer
(617) 229-6812
sjain@aurora.aero
Business Contact:
Scott Hart
Financial Analyst
(617) 500-4892
shart@aurora.aero
Research Institution:
n/a
Abstract
This effort proposes to develop a toolset to evaluate the effectiveness of a safety-controller that monitors a learning-based autonomous control system. The traditional use of deterministic verification techniques for real-time monitoring is not feasible in the presence of uncertainty. This effort would advance the recommended verification techniques to include non-deterministic approaches. The output would be to develop a Mathworks-based toolset to evaluate the run-time safety controller using advanced verification techniques. The inputs to the tool would allow for unexpected / unknown inputs into the system under test to better reflect real world conditions. The effort increases the fidelity of the verification technique and evaluates a learning-based trajectory planner with a safety controller currently used to operate a helicopter. This effort examines the use of boundary certificates and other approaches to further improve the detection of issues that traverse a safety boundary.

* information listed above is at the time of submission.

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