2414567
Project Grant
Overview
Grant Description
Sttr phase I: drone localization near and manipulation control in contact with power lines -the broader/commercial impact of this small business technology transfer phase I project is to alleviate the 2TW backlog of renewable energy projects desiring to connect to the power grid by installing and replacing dynamic-line rating (DLR) sensors on power lines with a novel cyclorotor drone.
Currently, such (un)installation tasks are being performed manually with the help of helicopters, cranes, scaffolding, and/or rope access. Such manipulations are dangerous since, for example, a helicopter would be at low altitude where it would be impossible to recover from an engine failure and would have substantial risk of colliding with the line.
On the other hand, conventional multicopter drones cannot perform heavy sensor installations. Specifically, they move by first pitching or rolling (underactuated motion), which hampers their ability to counter wind disturbances. This project will develop (i) techniques for localizing the drone with respect to power lines and (ii) control strategies that enable installation, removal, and maintenance of DLR sensors.
The work proposed in this project is to use innovative algorithms to navigate a cyclorotor-based drone to a power line based on the measurements of the electric and magnetic fields around power lines. This state-estimation technique around power lines is robust, using only the root-mean square (RMS) electric/magnetic field that is present around the power lines naturally due to the flow of power through them.
In parallel, a control system will be developed to bring the drone stably into contact with a power line to install and uninstall dynamic-line rating (DLR) sensors, bird-diverters, and other line products. This control system will be designed to seamlessly handle any abrupt transitions from free-flight to contact with a power line.
Upon successful completion, the project will provide an efficient method of installing and replacing power line sensors, bird diverters, and other line components. The IoT line sensors can help alleviate transmission congestion, allow increased penetration of renewable energy, and decrease wildfire risk.
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.- Subawards are planned for this award.
Currently, such (un)installation tasks are being performed manually with the help of helicopters, cranes, scaffolding, and/or rope access. Such manipulations are dangerous since, for example, a helicopter would be at low altitude where it would be impossible to recover from an engine failure and would have substantial risk of colliding with the line.
On the other hand, conventional multicopter drones cannot perform heavy sensor installations. Specifically, they move by first pitching or rolling (underactuated motion), which hampers their ability to counter wind disturbances. This project will develop (i) techniques for localizing the drone with respect to power lines and (ii) control strategies that enable installation, removal, and maintenance of DLR sensors.
The work proposed in this project is to use innovative algorithms to navigate a cyclorotor-based drone to a power line based on the measurements of the electric and magnetic fields around power lines. This state-estimation technique around power lines is robust, using only the root-mean square (RMS) electric/magnetic field that is present around the power lines naturally due to the flow of power through them.
In parallel, a control system will be developed to bring the drone stably into contact with a power line to install and uninstall dynamic-line rating (DLR) sensors, bird-diverters, and other line products. This control system will be designed to seamlessly handle any abrupt transitions from free-flight to contact with a power line.
Upon successful completion, the project will provide an efficient method of installing and replacing power line sensors, bird diverters, and other line components. The IoT line sensors can help alleviate transmission congestion, allow increased penetration of renewable energy, and decrease wildfire risk.
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.- Subawards are planned for this award.
Awardee
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "NSF SMALL BUSINESS INNOVATION RESEARCH (SBIR)/ SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAMS PHASE I", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF23515
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Boise,
Idaho
83716-9134
United States
Geographic Scope
Single Zip Code
Pitch Aeronautics was awarded
Project Grant 2414567
worth $274,853
from National Science Foundation in July 2024 with work to be completed primarily in Boise Idaho United States.
The grant
has a duration of 1 year and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
The Project Grant was awarded through grant opportunity NSF Small Business Innovation Research / Small Business Technology Transfer Phase I Programs.
SBIR Details
Research Type
STTR Phase I
Title
STTR Phase I: Drone Localization Near and Manipulation Control in Contact with Power Lines
Abstract
The broader/commercial impact of this Small Business Technology Transfer Phase I project is to alleviate the 2TW backlog of renewable energy projects desiring to connect to the power grid by installing and replacing dynamic-line rating (DLR) sensors on power lines with a novel cyclorotor drone. Currently, such (un)installation tasks are being performed manually with the help of helicopters, cranes, scaffolding, and/or rope access. Such manipulations are dangerous since, for example, a helicopter would be at low altitude where it would be impossible to recover from an engine failure and would have substantial risk of colliding with the line. On the other hand, conventional multicopter drones cannot perform heavy sensor installations. Specifically, they move by first pitching or rolling (underactuated motion), which hampers their ability to counter wind disturbances. This project will develop (i) techniques for localizing the drone with respect to power lines and (ii) control strategies that enable installation, removal, and maintenance of DLR sensors.
The work proposed in this project is to use innovative algorithms to navigate a cyclorotor-based drone to a power line based on the measurements of the electric and magnetic fields around power lines. This state-estimation technique around power lines is robust, using only the root-mean square (rms) electric/magnetic field that is present around the power lines naturally due to the flow of power through them. In parallel, a control system will be developed to bring the drone stably into contact with a power line to install and uninstall dynamic-line rating (DLR) sensors, bird-diverters, and other line products. This control system will be designed to seamlessly handle any abrupt transitions from free-flight to contact with a power line. Upon successful completion, the project will provide an efficient method of installing and replacing power line sensors, bird diverters, and other line components. The IoT line sensors can help alleviate transmission congestion, allow increased penetration of renewable energy, and decrease wildfire risk.
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.
Topic Code
MO
Solicitation Number
NSF 23-515
Status
(Complete)
Last Modified 6/20/24
Period of Performance
7/1/24
Start Date
6/30/25
End Date
Funding Split
$274.9K
Federal Obligation
$0.0
Non-Federal Obligation
$274.9K
Total Obligated
Activity Timeline
Subgrant Awards
Disclosed subgrants for 2414567
Additional Detail
Award ID FAIN
2414567
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
JU1CUPU6FEY4
Awardee CAGE
9CWS1
Performance District
ID-02
Senators
James Risch
Michael Crapo
Michael Crapo
Modified: 6/20/24