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Charles River Analytics, Inc. SBIR Phase I Award, June 2020

A SBIR Phase I contract was awarded to Charles River Analytics in June, 2020 for $146,500.0 USD from the U.S. Department of Defense and United States Navy.

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Contents

sbir.gov/node/1933739
Is a
SBIR/STTR Awards
SBIR/STTR Awards

SBIR/STTR Award attributes

SBIR/STTR Award Recipient
Charles River Analytics
Charles River Analytics
0
Government Agency
U.S. Department of Defense
U.S. Department of Defense
0
Government Branch
United States Navy
United States Navy
0
Award Type
SBIR0
Contract Number (US Government)
N68335-20-C-06720
Award Phase
Phase I0
Award Amount (USD)
146,5000
Date Awarded
June 19, 2020
0
End Date
December 16, 2020
0
Abstract

Unmanned surface vehicles (USVs) can keep people out of harm’s way, but the unmanned vehicles themselves can become a target or liability. On one hand, our unmanned vehicles must remain covert and avoid the vessels of pirates and hostile nations in open waters. Conversely, in peaceful times or regions, USVs can pose an unintentional risk of collision with non-combatant craft (for example, shipping and fishing vessels). Onboard manned vessels, people can actively make decisions about how exposed or covert the vessel and its crew should be. One solution for USVs is for a remote operator to make those same decisions. However, the very act of communication to a remote operator may increase the USV’s exposure. Therefore, the USV must be allowed to make its own decisions autonomously to manage its signature profile. To address these challenges, Charles River Analytics proposes to develop Management of Intelligent Navigation for Condition-based Ocean Safe Transit (MIN-COST). MIN-COST is an autonomy software tool that uses an adaptive behavior-based approach to actively manage USV signatures to either increase or decrease exposure based on the current situation. MIN-COST will enable a USV to operate in the guidance and constraints previously provided by a remote operator, but will adapt the specific actions based on current reality without relying on real-time operator input or human assistance.

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