Autonomous Seafloor Mapping System for Unmanned Undersea Vehicles

Award Information
Agency:
Department of Defense
Branch
Navy
Amount:
$69,963.00
Award Year:
2009
Program:
SBIR
Phase:
Phase I
Contract:
N66001-09-M-1032
Award Id:
92594
Agency Tracking Number:
N091-088-0771
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
BARRON ASSOC., INC. (Currently BARRON ASSOCIATES, INC.)
1410 Sachem Place, Suite 202, Charlottesville, VA, 22901
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
120839477
Principal Investigator:
Jason Burkholder
Principal Research Scient
(434) 973-1215
burkholder@bainet.com
Business Contact:
Connie Hoover
General Manager
(434) 973-1215
barron@bainet.com
Research Institution:
n/a
Abstract
Optimal seafloor mapping via an unmanned undersea vehicle (UUV) requires the solution and integration of two underlying problems: (1) autonomous underwater navigation and path planning and (2) accurate seafloor sensing and map construction. Each of these problems has been studied extensively; however, as stated in the solicitation, some major technology development areas requiring innovative solutions persist. Barron Associates, Inc. and its research partners have the expertise, resources, and infrastructure necessary to meet the overall goal of the proposed SBIR effort, which is to develop and demonstrate at-sea, within the time and budget constraints of the first two phases of the program, a complete and practical Autonomous Seafloor Mapping System (ASMS) for UUVs. The ASMS will feature practical enhancements to the current state-of-the-art in each of the problematic areas identified in the solicitation. The proposed ASMS will feature adaptive-autonomous survey schemes enabled by innovative methods to correct navigation errors and co-registered multi-beam side scan and bathymetry data that are precisely synchronized with the navigation system to facilitate simultaneous localization and mapping (SLAM). The Phase I effort will focus primarily on implementation and simulation of the adaptive-autonomous path planning component and the navigation error modeling and correction algorithms.

* information listed above is at the time of submission.

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