Demonstrated Environment/Harware Cooperationfor Expanded Riverine Coverage

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$69,960.00
Award Year:
2010
Program:
STTR
Phase:
Phase I
Contract:
N00014-10-M-0335
Award Id:
95121
Agency Tracking Number:
N10A-024-0514
Solicitation Year:
n/a
Solicitation Topic Code:
NAVY 10T024
Solicitation Number:
n/a
Small Business Information
1410 Sachem Place, Suite 202, Charlottesville, VA, 22901
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
120839477
Principal Investigator:
Nathan Richards
Research Scientist
(434) 973-1215
barron@bainet.com
Business Contact:
Connie Hoover
General Manager
(434) 973-1215
barron@bainet.com
Research Institution:
Virginia Tech
John C Rudd
1880 Pratt Drive
Suite 2006
Blacksburg, VA, 24060
(540) 231-5281
Nonprofit college or university
Abstract
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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