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Reinforcement Learning Algorithms for Unmanned Aerial Vehicles

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-19-C-0798
Agency Tracking Number: N192-062-0070
Amount: $139,995.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N192-062
Solicitation Number: 19.2
Solicitation Year: 2019
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-09-17
Award End Date (Contract End Date): 2020-03-24
Small Business Information
1845 West 205th Street
Torrance, CA 90501
United States
DUNS: 153865951
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Armen Gholian, Ph.D. Armen Gholian, Ph.D.
 Sr. Research Scientist
 (310) 320-3088
Business Contact
 Keith Baker
Phone: (424) 835-9475
Research Institution

To address the Navy’s need for artificial neural networks (ANN)-centric reinforcement learning (RL) algorithms that provide unmanned aerial vehicles (UAVs) with the capability to autonomously conduct flight from takeoff to landing, Physical Optics Corporation (POC) proposes to develop a new Reinforcement Learning Algorithms for Unmanned Aerial Vehicles (REALUAV) algorithm suite. It is based on a unique integration of state-of-the-art deep RL algorithms and techniques that enable the REALUAV agent to learn and master a set of diverse tasks sequentially or simultaneously and, given a flight profile, accomplishes a mission within an unknown environment optimally. REALUAV offers autonomous operation of UAVs, modifiable in real-time by a human-in-the-loop, which directly address the PMA268 Navy Unmanned Combat Air System Demonstration program requirements. In Phase I, POC will design, develop, and demonstrate the feasibility of REALUAV by reaching technology readiness level (TRL)-3. In Phase II, POC plans to develop a REALUAV prototype algorithm suite reaching TRL-5/-6.

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

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