SBIR-STTR Award

SBIR Phase II FA8649-19-9-9035: Remote Video Training Platform, TACFI SBIR Phase II Altus AFB, OK
Award last edited on: 5/17/2023

Sponsored Program
SBIR
Awarding Agency
DOD : AF
Total Award Amount
$4,760,558
Award Phase
2
Solicitation Topic Code
AF191-004
Principal Investigator
Joseph Weaver

Company Information

Ario Technologies Inc

400 Granby Street Suite 108
Norfolk, VA 23510
   (540) 230-5836
   info@ario.com
   www.ario.com
Location: Single
Congr. District: 03
County: Norfolk city

Phase I

Contract Number: FA3002-19-P-A140
Start Date: 3/6/2019    Completed: 3/6/2020
Phase I year
2019
Phase I Amount
$60,561
Mixed reality visualization for aircraft predictive maintenance and aircraft Internet of Things (IoT) data. We developed a secure platform called Ario to scale augmented reality to waypoint, manage and associate data with spatial context. Our ario platform increases productivity, connects data with equipment, decreases training time and captures knowledge. Ario would provide a bridge between field workers and data streams to allow for improved readiness for aircraft. We have spent the last year piloting our applications with great success to energy, defense contractors, and military sectors. In 2019 we are expanding our platform to benefit the retail, construction, and Internet of Things (IoT) markets. Connecting IoT sensors to aircraft the ario platform provides an intuitive interface for users to interact and interpret data.

Phase II

Contract Number: FA8649-19-9-9035
Start Date: 8/2/2019    Completed: 8/2/2021
Phase II year
2019
(last award dollars: 2022)
Phase II Amount
$4,699,997

Ario platform will allow delivery of tasking and predictive maintenance data through mobile mixed reality devices to the physical aircraft. Ario platform will allow maintenance data to be projected on relevant equipment. Way pointing to data sources will decrease time spent locating proper information. Communication with experts remotely can reduce errors and reduce the need for traveling to physical site for analysis of equipment status, thereby reducing downtime. Analytics will be captured for use to reevaluate training requirements.