SBIR/STTR Research Award
Global Optimization of Graph Learning Structures (GOGLS)
Award amount
$167,410Program and phase
SBIR · Phase IAward year
2022Award details
- Awarding agency
- Department of Defense
- Branch
- Defense Threat Reduction Agency
- Recipient company
- MTRI INC, dba Balto Innovations
- Proposal award date
- 2022-03-28
- Contract end date
- 2022-10-31
- UEI
- ECJGKBV4G2P8
- Solicitation number
- 21.2
- Topic code
- DTRA212-002
Research abstract
Global Optimization of Graph Learning Structures (GOGLS) is a platform- agnostic method of globally optimizing search graphs from individual autonomous agents using techniques derived from federated learning. The GOGLS mission and focus will be to provide a learning framework for robots to share search information and in essence, see through the eyes of their neighbors. The GOGLS system relies on passing and fusing the latest high level model of the environment between robotic autonomous systems instead of the low level data. This allows the group to explore and learn about the environment in a federated learning inspired manner without the need for large online data transfers.
Technology keywords
Not reported
Other awards by this recipient
Verified by the same UEI. These are other public SBIR/STTR award pages, not a merged company record.
- 3D ATR plus Motion
- Sub-Wavelength All Motions Information (SWAMI)
- Tool for Autonomous Terrain Exploration of Remote Space
- Low-Cost Electronic Warfare Training Unmanned System (LoCEWTUS)
- Online and Offline Terrain Strength Estimation Using Remote Sensing for Ground Vehicle Mobility - Phase II
Awards with the same topic code
Verified by an exact topic code match. This is not a text-similarity match.
Research funding history
13 SBIR/STTR records share this UEI (2020–2025). Reported award amounts total $4,885,562. Reported amounts are not federal obligations.
Phases: Phase I, Phase II
Agencies: Department of Defense, Department of Transportation, National Aeronautics and Space Administration