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Ship-based Operations for UAS Swarms with Autonomous Pinniped Homing Over Nasty Environments (SOUSAPHONE)

Award Information
Agency: Department of Commerce
Branch: National Oceanic and Atmospheric Administration
Contract: NA20OAR0210101
Agency Tracking Number: NA20OAR0210101
Amount: $119,953.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: 9.5.01
Solicitation Number: NOAA-OAR-OAR-TPO-2019-2005899
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-01-01
Award End Date (Contract End Date): 2020-08-31
Small Business Information
625 Mount Auburn Street, Cambridge, MA, 02138
DUNS: 115243701
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Dan Stouch
 (617) 491-3474
Business Contact
 Yvonne Fuller
Title: Director of Proposals
Phone: (617) 491-3474
Research Institution
Small Unmanned aircraft systems (UAS) play a critical and growing role in government, military, commercial, and scientific operations across a range of missions such as weather monitoring, natural disaster assessment, surveillance, and infrastructure inspection. Their versatility, maneuverability, and dependability, coupled with their ability to keep operators out of harm’s way, make them critical assets in a wide range of domains, but especially so in hostile, unpredictable, and dynamic environments. To operate reliably and effectively among other aircraft, replace manned aircraft missions with unmanned systems, and enable effective beyond line of sight (BLOS) operations, new capabilities are needed. Charles River proposes to develop a concept design, perform a feasibility assessment, and conduct limited risk reduction experiments for Ship-based Operations for UAS Swarms with Autonomous Pinniped Homing Over Nasty Environments (SOUSAPHONE). SOUSAPHONE is an aircraft and autopilot agnostic software framework that enables multiple ship-launched UAS to safely coordinate semi-autonomous BLOS wildlife survey operations (e.g., for pinnipeds) using advanced artificial intelligence and machine learning (AI/ML)-based computer vision and relative navigation techniques in challenging environments such as the Alaskan arctic.

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

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