2304303
Project Grant
Overview
Grant Description
Sttr Phase I: Subcanopy 3D Forest Mapping by Uncrewed Aerial Vehicle - The broader/commercial impact of this Small Business Technology Transfer (STTR) Phase I project is a tool to help mitigate the wildfire crisis in the Western United States. Prescribed burns are one of the most effective ways to prevent the uncontrolled, large-scale wildfires that devastate entire ecosystems, communities, and economies, but the environmental assessment required for burns may delay the burn by months or years due to overburdened agencies.
The innovation addresses this pain point with an automated survey solution, reducing both the paperwork burden and the potential for error in burn area vegetation mapping and spatial fire modeling. The solution ensures that prescribed burn plans use the best available vegetation data, providing fire managers with an accurate prediction of expected fire behavior for determining control strategy, staffing, and resources.
This innovation would be the first automated prescribed burn spatial fire modeling solution using an autonomous unmanned aerial vehicle (UAV). This innovation meets the STTR program's focus on unproven, high-impact innovations because sub-canopy mapping by UAV is a cutting-edge application of autonomous flight, with challenges in optimization and decision-making.
The application of UAV technology to prescribed burn environmental assessments will help to address the growing wildfire crisis in the United States by reducing the delay between the decision to burn at a selected site and the execution of the burn. The technical hurdles to be addressed by the proposed project include both real-time, sub-canopy 3D mapping and optimization for constraints across sensor requirements, cluttered environment exploration, and SWAP (size, weight, and power) limitations.
The goals are to produce a high-fidelity, open-source UAV exploration environment, to implement 3D mapping on a resource-constrained computer that meets UAV payload requirements, to incorporate species-specific decision-making criteria into the UAV exploration algorithm, and to conduct validation testing to verify technical and early commercial feasibility.
To achieve these goals, the development plan includes a series of milestones following a test-driven, development project management strategy, including simulation testing (software in the loop), tabletop testing (hardware in the loop), and field tests.
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.
The innovation addresses this pain point with an automated survey solution, reducing both the paperwork burden and the potential for error in burn area vegetation mapping and spatial fire modeling. The solution ensures that prescribed burn plans use the best available vegetation data, providing fire managers with an accurate prediction of expected fire behavior for determining control strategy, staffing, and resources.
This innovation would be the first automated prescribed burn spatial fire modeling solution using an autonomous unmanned aerial vehicle (UAV). This innovation meets the STTR program's focus on unproven, high-impact innovations because sub-canopy mapping by UAV is a cutting-edge application of autonomous flight, with challenges in optimization and decision-making.
The application of UAV technology to prescribed burn environmental assessments will help to address the growing wildfire crisis in the United States by reducing the delay between the decision to burn at a selected site and the execution of the burn. The technical hurdles to be addressed by the proposed project include both real-time, sub-canopy 3D mapping and optimization for constraints across sensor requirements, cluttered environment exploration, and SWAP (size, weight, and power) limitations.
The goals are to produce a high-fidelity, open-source UAV exploration environment, to implement 3D mapping on a resource-constrained computer that meets UAV payload requirements, to incorporate species-specific decision-making criteria into the UAV exploration algorithm, and to conduct validation testing to verify technical and early commercial feasibility.
To achieve these goals, the development plan includes a series of milestones following a test-driven, development project management strategy, including simulation testing (software in the loop), tabletop testing (hardware in the loop), and field tests.
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.
Awardee
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Pittsburgh,
Pennsylvania
15213-3815
United States
Geographic Scope
Single Zip Code
Related Opportunity
None
Robotics 88 was awarded
Project Grant 2304303
worth $274,929
from National Science Foundation in May 2023 with work to be completed primarily in Pittsburgh Pennsylvania United States.
The grant
has a duration of 6 months and
was awarded through assistance program 47.084 NSF Technology, Innovation, and Partnerships.
SBIR Details
Research Type
STTR Phase I
Title
STTR Phase I:Subcanopy 3D forest mapping by uncrewed aerial vehicle
Abstract
The broader/commercial impact of this Small Business Technology Transfer (STTR) Phase I project is a tool to help mitigate the wildfire crisis in the western United States. Prescribed burns are one of the most effective ways to prevent the uncontrolled, large-scale wildfires that devastate entire ecosystems, communities, and economies, but the environmental assessment required for burns may delay the burn by months or years due to overburdened agencies. The innovation addresses this pain point with an automated survey solution, reducing both the paperwork burden and the potential for error in burn area vegetation mapping and spatial fire modeling. The solution ensures that prescribed burn plans use the best available vegetation data, providing fire managers with an accurate prediction of expected fire behavior for determining control strategy, staffing, and resources. This innovation would be the first automated prescribed burn spatial fire modeling solution using an autonomous Unmanned Aerial Vehicle (UAV). This innovation meets the STTR program’s focus on unproven, high-impact innovations because sub-canopy mapping by UAV is a cutting-edge application of autonomous flight, with challenges in optimization and decision-making. The application of UAV technology to prescribed burn environmental assessments will help to address the growing wildfire crisis in the United States by reducing the delay between the decision to burn at a selected site and the execution of the burn. _x000D_ _x000D_ The technical hurdles to be addressed by the proposed project include both real-time, sub-canopy 3D mapping and optimization for constraints across sensor requirements, cluttered environment exploration, and SWAP (size, weight, and power) limitations. The goals are to produce a high-fidelity, open-source. UAV exploration environment, to implement 3D mapping on a resource-constrained computer that meets UAV payload requirements, to incorporate species-specific decision-making criteria into the UAV exploration algorithm, and to conduct validation testing to verify technical and early commercial feasibility. To achieve these goals, the development plan includes a series of milestones following a test-driven, development project management strategy, including simulation testing (software in the loop), tabletop testing (hardware in the loop), and field tests._x000D_ _x000D_ 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
R
Solicitation Number
NSF 22-551
Status
(Complete)
Last Modified 5/19/23
Period of Performance
5/15/23
Start Date
11/30/23
End Date
Funding Split
$274.9K
Federal Obligation
$0.0
Non-Federal Obligation
$274.9K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2304303
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
VNNMU85L26M7
Awardee CAGE
97V04
Performance District
12
Senators
Robert Casey
John Fetterman
John Fetterman
Representative
Summer Lee
Budget Funding
Federal Account | Budget Subfunction | Object Class | Total | Percentage |
---|---|---|---|---|
Research and Related Activities, National Science Foundation (049-0100) | General science and basic research | Grants, subsidies, and contributions (41.0) | $274,929 | 100% |
Modified: 5/19/23