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Completed STANDARD GRANT National Science Foundation (US)

SBIR Phase I: Autonomous Living Assistant

$2.56M USD

Funder National Science Foundation (US)
Recipient Organization Rivieh, Inc.
Country United States
Start Date Jul 01, 2021
End Date Dec 31, 2022
Duration 548 days
Number of Grantees 1
Roles Principal Investigator
Data Source National Science Foundation (US)
Grant ID 2051981
Grant Description

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is a wider adoption of autonomous technologies among residential buildings. This research proposes evaluating the use of advanced sensor technologies and machine learning algorithms to enable inexpensive hardware infrastructures to control the conditions in indoor space autonomously.

This technology fits well within the capacity of residential buildings. One main benefit of indoor automation is deep energy savings. The Department of Energy estimates a 5% reduction of the country's total energy bill if existing residential buildings save 25% of their energy consumption.

The autonomous controller evaluated in this project has the potential to reach this goal, saving the nation up to $63 billion and the planet 284 million metric tons of carbon dioxide emissions. Alongside energy efficiency, the associated societal benefits of the proposed project can potentially save lives. The technologies may spur a higher level of security protection among many unprotected residential spaces.

Additionally, tracking occupants’ habits may enable alerts to caregivers or family members in critical emergency situations. This can be beneficial for the aging population that wants to stay within the comfort of their own home.

This Small Business Innovation Research (SBIR) Phase I project will advance the scientific knowledge required to approach full autonomous control in indoor spaces. The use of advanced perception sensors in buildings will be evaluated. Their rich data stream will be utilized to develop advanced algorithms for autonomous control of indoor spaces.

Advances in sensing technologies and machine learning algorithms will be used to instigate technological evolution in autonomous building systems. A proof of concept will be developed to mitigate the unique risks associated caused by applying autonomous control in indoor spaces, specifically: achieving advanced perception, self-programmability, and reliability.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

All Grantees

Rivieh, Inc.

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