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DNA-mRNA: Digital twin Novel Architecture study with Machine learning Research for Naval Applications (Phase 2)

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: HR001122C0183
Agency Tracking Number: D2-2701
Amount: $1,499,877.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: HR001121S0007-24
Solicitation Number: HR001121S0007.I
Timeline
Solicitation Year: 2021
Award Year: 2022
Award Start Date (Proposal Award Date): 2022-09-22
Award End Date (Contract End Date): 2024-05-31
Small Business Information
500 West Cummings Park Suite 3000
Woburn, MA 01801-1111
United States
DUNS: 859244204
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: Yes
Principal Investigator
 Mitchell Colby
 (781) 933-5355
 Mitchell.Colby@ssci.com
Business Contact
 Lora Loyall
Phone: (781) 933-5355
Email: contracts@ssci.com
Research Institution
N/A
Abstract

In order to maintain U.S. military advantages, the U.S. Naval Service must have widespread adoption of autonomous assets such as autonomous UUVs.  However, autonomous UUVs present a variety of challenges in the maritime environment, such as (1) a requirement that the commander can predict, control, and understand an autonomous fleet, even under limited communications, (2) autonomous systems must be adaptable and resilient to changing platform characteristics, e.g., platform damage/degradation, and (3) autonomous systems at scale must minimize communication requirements while still coordinating with other platforms at the edge and ensuring the commander has current situational awareness of the fleet.  We propose DNA-mRNA (Digital twin Novel Architecture Study with Machine Learning Research for Naval Applications).  Under the Phase I effort, we demonstrated that digital twins augmented with Machine Learning and Autonomy techniques could effectively reduce communication requirements in a multi-UUV coordinating system, improve system resiliency and adaptability to unexpected events such as platform damage or degradation, and improved predictability and situational awareness for a commander.  In the proposed Phase II effort, we will build a full prototype of this system concept, ensuring that the autonomy stack is UMAA compliant and can quickly be transitioned to physical hardware.  This involves generating scalable digital twins for large multiagent systems, a composable high-fidelity simulation environment that can be tuned for a specific set of platforms, a digital twin-enabled communications optimization approach, resilient collaborative maritime autonomy leveraging multiagent learning-based control, and digital twin-enabled explainability and predictability of autonomous systems.

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

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