Adversarial Reasoning for Advanced Unmanned Teaming (AVERT)

Award Information
Agency:
Department of Defense
Branch
Army
Amount:
$730,000.00
Award Year:
2012
Program:
SBIR
Phase:
Phase II
Contract:
W15P7T-12-C-H204
Award Id:
n/a
Agency Tracking Number:
A2-4800
Solicitation Year:
2010
Solicitation Topic Code:
A10-091
Solicitation Number:
2010.2
Small Business Information
6800 Cortona Drive, Goleta, CA, -
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
054672662
Principal Investigator:
GaemusCollins
Senior Analyst
(805) 968-6787
gcollins@toyon.com
Business Contact:
MarcellaLindbery
Director
(805) 968-6787
mlindbery@toyon.com
Research Institute:
Stub




Abstract
Toyon proposes to develop adversarial target prediction algorithms and a mission execution framework that supports collaborative teaming of airborne and ground-based Unmanned Systems (UMS) to enable tracking and surveillance of evasive targets. The proposed AVERT system will include automatic video processing, global fusion and tracking, adversarial reasoning and prediction logic, automatic UMS feedback control, and an Android-based application for a handheld device. Using Toyon's custom GeoTrack embedded hardware, the video processing and global fusion and tracking capabilities will exist in parallel on-board the UMS and on the ground station, adding flexibility to the system architecture. The on-board processing enables AVERT to operate with a decentralized architecture; each UMS can operate autonomously, but a wireless network can support data sharing for collaborative actions between several UMS. The on-board functionality will also enable the Android app and device to receive video, telemetry, and track data directly from all UMS within radio range, without hopping through a ground station. The handheld operator can also input ISR requests to the handheld that will be processed by the UMS. Toyon's field-proven video processing algorithms will automatically detect moving targets in video. Target detections will be automatically geo-registered using sensor state data from the platform GPS and IMU, then fused into target tracks in the ground plane, enabling autonomous tracking even in urban and complex environments. Ground plane tracks will be sent to the UMS operator for viewing, and passed on to UMS control algorithms. Our control algorithms will use adversarial modeling to anticipate potentially evasive actions of the target, then optimize the platform trajectory and sensor orientation to obtain clear-line-of-sight to the target and maintain persistent surveillance.

* information listed above is at the time of submission.

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