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Artificial Intelligence/Machine Learning to Improve Maneuver of Robotic/Autonomous Systems

Award Information
Agency: Department of Defense
Branch: Army
Contract: W56KGU-17-C-0066
Agency Tracking Number: A17A-019-0091
Amount: $149,185.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: A17A-T019
Solicitation Number: 2017.0
Solicitation Year: 2017
Award Year: 2017
Award Start Date (Proposal Award Date): 2017-09-25
Award End Date (Contract End Date): 2018-02-24
Small Business Information
990 North 8000 West
Petersboro, UT 84325
United States
DUNS: 964299791
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Jeff Ferrin
 Research Team Lead
 (435) 227-7421
Business Contact
 Raeghn Torrie
Phone: (435) 227-1064
Research Institution
 Georgia Institute of Technology
 Panagiotis Tsiotras
North Ave NW, Atlanta, GA 30332
Atlanta, GA 30332
United States

 () -
 Nonprofit College or University

Robotic autonomous systems (RAS) are currently being used for many different applications using a wide variety of vehicle platforms. The environments in which RAS are being used are becoming increasingly complex. Vehicle path planning and control is challenging in environments with many obstacles and uneven terrain. This proposed research will develop and compare multiple techniques to improve vehicle response through difficult terrain. The current state-of-the-art will be researched to find methods that can be used to improve vehicle maneuverability in difficult terrain. Two other methods will be compared. These methods are nonlinear model predictive control (NMPC) and a machine learning method associated with trajectory generation. This work will compare these methods in simulation and then implement and test the best method on a fully automated Ford Escape.

* Information listed above is at the time of submission. *

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