You are here

Swarms with Autonomous Pinniped Homing Over Nasty Environments (SOUSAPHONE)

Award Information
Agency: Department of Commerce
Branch: National Oceanic and Atmospheric Administration
Contract: NA21OAR0210109
Agency Tracking Number: 2926459
Amount: $399,965.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: 9.5.01
Solicitation Number: NOAA-OAR-OAR-TPO-2020-2006595
Solicitation Year: 2020
Award Year: 2021
Award Start Date (Proposal Award Date): 2021-02-01
Award End Date (Contract End Date): 2023-01-31
Small Business Information
625 Mount Auburn Street
Cambridge, MA 02138
United States
DUNS: 115243701
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Daniel  Stouch
 Principal Scientist
 (617) 491-3474
Business Contact
 Mark Felix
Title: Director of Contracts
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 dangerous, dynamic, and unpredictable environments. For UAS to operate reliably and effectively among other aircraft, replace manned aircraft on missions, and enable effective beyond visual line of sight (BVLOS) operations, new capabilities are needed. Charles River proposes to implement a concept design, develop a prototype system, 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 BVLOS 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. *

US Flag An Official Website of the United States Government