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

Award Information
Agency: Department of Defense
Branch: Office of the Secretary of Defense
Contract: FA8650-14-M-2454
Agency Tracking Number: O133-HS2-1038
Amount: $149,985.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: OSD13-HS2
Solicitation Number: 2013.3
Solicitation Year: 2013
Award Year: 2014
Award Start Date (Proposal Award Date): 2014-01-09
Award End Date (Contract End Date): 2014-10-15
Small Business Information
9950 Wakeman Drive
Manassas, VA 20110-
United States
DUNS: 604717165
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Sacin Jain
 Sr. GNC Engineer
 (617) 229-6812
Business Contact
 Scott Hart
Title: Financial Analyst
Phone: (617) 500-4892
Research Institution

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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