PROWL: Perception for Robotic Operation over Widespread Lighting

Award Information
Agency:
Department of Defense
Branch
n/a
Amount:
$711,615.00
Award Year:
2013
Program:
SBIR
Phase:
Phase II
Contract:
W56HZV-13-C-0058
Award Id:
n/a
Agency Tracking Number:
A2-5186
Solicitation Year:
2010
Solicitation Topic Code:
A10-175
Solicitation Number:
2010.3
Small Business Information
100 Northeast Loop 410, Suite 520, San Antonio, TX, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
193786014
Principal Investigator:
PatrickBeeson
Senior Scientist
(281) 461-7886
pbeeson@traclabs.com
Business Contact:
DavidKortenkamp
President/CEO
(281) 461-7886
korten@traclabs.com
Research Institute:
Stub




Abstract
In Phase I we designed and evaluated a thermal stereo prototype; however, rather than focusing on a nighttime-only deliverable in Phase II, we propose to combine our Phase I experience with stereo thermal sensing and our decades long experience with traditional outdoor stereo sensing to deliver a 24-hour, passive-sensing payload. We call this self-contained system PROWL (Perception for Robotic Operation over Widespread Lighting). PROWL is designed to be a compact, light-weight, low-power, self-contained module that can be added to any robot platform. Long-term, we expect to integrate our system with specific unmanned military robots, including PackBot, Talon, ANDROS, and MULE platforms. Our Phase II deliverable will be designed specifically as a PackBot payload with help from our partners at iRobot. The PROWL deliverable should provide both monochrome images of the visible spectrum and thermal images in the LWIR spectrum. We expect to demonstrate visualization of PROWL data on the PackBot operator control unit (OCU), which should display 2D camera info with optional 3D and temperature overlays when desired. PROWL will also contain onboard computing that will facilitate researchers and developers to run their own algorithms for processing of 2D images and 3D point clouds, and publish data over a flexible API. We expect to demonstrate this with some of our own algorithms for obstacle detection and leader following in a broad spectrum of illumination conditions, including total darkness.

* information listed above is at the time of submission.

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