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Decision Support for Anomaly Detection and Recovery for Unmanned System (ADRUS)

Award Information
Agency: Department of Defense
Branch: Office of the Secretary of Defense
Contract: N00014-14-C-0279
Agency Tracking Number: O2-1479
Amount: $999,705.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: OSD12-AU1
Solicitation Number: 2012.3
Timeline
Solicitation Year: 2012
Award Year: 2014
Award Start Date (Proposal Award Date): 2014-09-05
Award End Date (Contract End Date): 2015-09-30
Small Business Information
9120 Beachway Lane
Springfield, VA 22153
United States
DUNS: 000000000
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: Yes
Principal Investigator
 Kalyan Gupta
 (855) 569-7373
 kalyan.gupta@knexusresearch.com
Business Contact
 Kalyan Gupta
Phone: (855) 569-7373
Email: kalyan.gupta@knexusresearch.com
Research Institution
N/A
Abstract

Deployment of unmanned systems continues to expand across a wide range of missions; for example, logistics and resupply missions, force application and protection, and improving battlespace awareness. Presently these unmanned systems run at the lowest of four possible levels of autonomy, that is, in a teleoperated mode, and each system typically requires multiple operators. To address this problem of operator scalability, investigations into approaches for human supervised autonomy were called for in this SBIR. In Phase I, we took a step toward addressing this capability gap by developing a decision support system for Anomaly Detection and Recovery of Unmanned Systems (ADRUS). In particular, we demonstrated that ADRUS could successfully handle unexpected events or anomalies and replan to recover from them. Our demonstration included a proof-of-concept prototype implementation and its performance in simulated logistics and resupply missions. In Phase II, we will continue algorithmic development of anomaly detection, mission risk analysis, and replanning reasoning services to meet the performance requirements of our target transition environments. Our approaches and extensions will include methods for improving reasoning accuracies, model coverage and fidelity, as well as the ability to learn and improve knowledge models by exploiting operator interactions and decisions data. We will implement and evaluate progressively mature versions of ADRUS throughout the performance period. We will conduct repeated tests and evaluations (T&E) in simulation using realistic models of target unmanned platforms. Based on T&E, we will characterize the robustness, scalability, and coverage of ADRUS. In addition, we will evaluate the operational effectiveness resulting from human supervisory control enabled by ADRUS. For these evaluations, we will engage application subject matter experts (SME) and operators from candidate transition programs. We have initiated discussions with prime performers from target programs developing these unmanned platforms and we will develop our transition requirements accordingly.

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

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