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Demonstrated Environment/Harware Cooperation for Expanded Riverine Coverage

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-10-M-0335
Agency Tracking Number: N10A-024-0514
Amount: $69,960.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N10A-T024
Solicitation Number: 2010.A
Solicitation Year: 2010
Award Year: 2010
Award Start Date (Proposal Award Date): 2010-06-28
Award End Date (Contract End Date): 2011-04-30
Small Business Information
1410 Sachem Place Suite 202
Charlottesville, VA 22901
United States
DUNS: 120839477
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Nathan Richards
 Research Scientist
 (434) 973-1215
Business Contact
 Connie Hoover
Title: General Manager
Phone: (434) 973-1215
Research Institution
 Virginia Tech
 John C Rudd
1880 Pratt Drive Suite 2006
Blacksburg, VA 24060
United States

 (540) 231-5281
 Nonprofit College or University

The Barron Associates/Virginia Tech team believes that intelligent use of ambient riverine environmental factors together with novel drifter design and low-energy articulation is the key to enabling large non-overlapping river coverage. The proposed phase I research program focuses on (1) identifying key river characteristics which may be leveraged by riverine drifters, (2) fabricating a novel riverine vehicle with a motion control system which respects and cooperates with the river environment, (3) on-line learning to update a priori environment models with new information based on observations, and (4) in-river demonstration of the composite design. The overarching goal of the proposed program of research and development is to leverage known and learned characteristics of the riverine environment to demonstrate efficient non-overlapping river coverage through intelligent vehicle design, mission planning, and real-time reaction to unforeseen characteristics. The team believes that an approach that explicitly considers and cooperates with the riverine environment has the potential to significantly outperform a system that treats the environment as a disturbance. Also, the ability to learn and update a priori models enables the system to function efficiently in situations where the environment has changed significantly or situations where little or no a priori information is available.

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

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