Advanced Detection, Classification, and Avoidance Toolkit (ADCAT)

Award Information
Agency:
Department of Defense
Amount:
$69,998.00
Program:
SBIR
Contract:
N65538-05-M-0030
Solitcitation Year:
2004
Solicitation Number:
2004.3
Branch:
Navy
Award Year:
2004
Phase:
Phase I
Agency Tracking Number:
N043-219-0919
Solicitation Topic Code:
N04-219
Small Business Information
21ST CENTURY SYSTEMS, INC.
12152 Windsor Hall Way, Herndon, VA, 20170
Hubzone Owned:
N
Woman Owned:
Y
Socially and Economically Disadvantaged:
N
Duns:
949183701
Principal Investigator
 Robert Woodley
 Scientist
 (573) 329-8526
 Robert.Woodley@21csi.com
Business Contact
 Lana Stoyen
Title: President
Phone: (571) 323-0080
Email: lana@21csi.com
Research Institution
N/A
Abstract
The need for autonomous unmanned vehicles is becoming more evident. Unmanned surface vehicles (USVs) provide benefits ranging from manpower reduction and force multiplication, to performing missions too dangerous for manned platforms. One of the most critical challenges is the avoidance of obstacles. 21st Century Systems, Incorporated (21CSI) is in the right place at the right time with regard to this topic. Through various SBIR projects for the Navy and other services and agencies, we have developed and refined many of the pieces required to meet this challenge and are pleased to propose to address it. Through our considerable decision support expertise gleaned from development projects on behalf of many DOD agencies and melded with a state-of-the-art image processing technique, we propose to provide state-of-the-art object detection and classification for situation awareness and object avoidance. We call our concept the Advanced Detection, Classification, and Avoidance Toolkit (ADCAT). The general nature of our proposed solution and its expected effectiveness will make it applicable to a wide range of FNC unmanned vehicles. The ADCAT enabling technology, utilizing a computationally efficient algorithm that is robust with respect to geometrical transformations and spatial and temporal variations, will permit object detection and classification for unmanned vehicles.

* information listed above is at the time of submission.

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