2322402
Project Grant
Overview
Grant Description
Sbir Phase I: Trajectory Optimizations and Learned Foliage Manipulation to Accelerate Throughput in Automated Strawberry Harvesting -The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to make automated harvesting of strawberries more efficient and effective and hence, more financially viable for growers to adopt.
Automated harvesters currently deployed in conventional strawberry farms cannot reliably handle peak-season conditions when strawberries are hidden below a thick plant canopy and where plants must be displaced to view and pick the fruit. This project will develop software to expand the set of conditions whereby automation can increase productivity.
Strawberries, the second most popular fruit in the United States, have the highest cost per acre to harvest because of their high touch harvesting process. Automating the harvesting process reduces labor needs, with the potential to either decrease costs and/or increase the quality of the fruit. Further, the project is expected to create high-skill jobs for American workers.
This project's main technical objective is to improve the quality and coverage of the map representation of strawberries and plants which will increase the number of harvested strawberries and the rate at which they are picked. The scope of the Phase I activity is to implement two software capabilities and to test them in simulation, laboratory, and field environments.
The first capability is a trajectory optimization module for a camera mounted to a robot manipulator. This technology will be designed to maximize information gain and to reduce localization uncertainty for strawberries while respecting kinematic and collision constraints for the motion of the robot arm. Success is to be measured by the rate of information gain relative to a na?ve precomputed scan.
The second is a trained neural network which estimates the parameters that best define a manipulation task plan for displacing foliage to maximize strawberry visibility and access for subsequent picking. Training and inference will be done in an end-to-end fashion, allowing an estimate of the value of a given task plan from color and depth camera observations of the scene. This contrasts with a conventional pipeline which doesn't make the most of the rich latent representations possible with neural networks.
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 not planned for this award.
Automated harvesters currently deployed in conventional strawberry farms cannot reliably handle peak-season conditions when strawberries are hidden below a thick plant canopy and where plants must be displaced to view and pick the fruit. This project will develop software to expand the set of conditions whereby automation can increase productivity.
Strawberries, the second most popular fruit in the United States, have the highest cost per acre to harvest because of their high touch harvesting process. Automating the harvesting process reduces labor needs, with the potential to either decrease costs and/or increase the quality of the fruit. Further, the project is expected to create high-skill jobs for American workers.
This project's main technical objective is to improve the quality and coverage of the map representation of strawberries and plants which will increase the number of harvested strawberries and the rate at which they are picked. The scope of the Phase I activity is to implement two software capabilities and to test them in simulation, laboratory, and field environments.
The first capability is a trajectory optimization module for a camera mounted to a robot manipulator. This technology will be designed to maximize information gain and to reduce localization uncertainty for strawberries while respecting kinematic and collision constraints for the motion of the robot arm. Success is to be measured by the rate of information gain relative to a na?ve precomputed scan.
The second is a trained neural network which estimates the parameters that best define a manipulation task plan for displacing foliage to maximize strawberry visibility and access for subsequent picking. Training and inference will be done in an end-to-end fashion, allowing an estimate of the value of a given task plan from color and depth camera observations of the scene. This contrasts with a conventional pipeline which doesn't make the most of the rich latent representations possible with neural networks.
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 not 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
La Canada Flintridge,
California
91011-2206
United States
Geographic Scope
Single Zip Code
L5 Automation was awarded
Project Grant 2322402
worth $275,000
from National Science Foundation in September 2023 with work to be completed primarily in La Canada Flintridge California United States.
The grant
has a duration of 5 months 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
SBIR Phase I
Title
SBIR Phase I:Trajectory Optimizations and Learned Foliage Manipulation to Accelerate Throughput in Automated Strawberry Harvesting
Abstract
The broader/commercial impact of this Small Business Innovation Research (SBIR) Phase I project is to make automated harvesting of strawberries more efficient and effective and hence, more financially viable for growers to adopt. Automated harvesters currently deployed in conventional strawberry farms cannot reliably handle peak-season conditions when strawberries are hidden below a thick plant canopy and where plants must be displaced to view and pick the fruit. This project will develop software to expand the set of conditions whereby automation can increase productivity. Strawberries, the second most popular fruit in the United States, have the highest cost per acre to harvest because of their high touch harvesting process. Automating the harvesting process reduces labor needs, with the potential to either decrease costs and / or increase the quality of the fruit. Further, the project is expected to create high-skill jobs for American workers. _x000D_ _x000D_ _x000D_ This project’s main technical objective is to improve the quality and coverage of the map representation of strawberries and plants which will increase the number of harvested strawberries and the rate at which they are picked. The scope of the Phase I activity is to implement two software capabilities and to test them in simulation, laboratory and field environments. The first capability is a trajectory optimization module for a camera mounted to a robot manipulator. This technology will be designed to maximize information gain and to reduce localization uncertainty for strawberries while respecting kinematic and collision constraints for the motion of the robot arm. Success is to be measured by the rate of information gain relative to a naïve precomputed scan. The second is a trained neural network which estimates the parameters that best define a manipulation task plan for displacing foliage to maximize strawberry visibility and access for subsequent picking. Training and inference will be done in an end-to-end fashion, allowing an estimate of the value of a given task plan from color and depth camera observations of the scene. This contrasts with a conventional pipeline which doesn’t make the most of the rich latent representations possible with neural networks._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 23-515
Status
(Complete)
Last Modified 9/22/23
Period of Performance
9/15/23
Start Date
2/29/24
End Date
Funding Split
$275.0K
Federal Obligation
$0.0
Non-Federal Obligation
$275.0K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2322402
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Small Business
Awarding Office
491503 TRANSLATIONAL IMPACTS
Funding Office
491503 TRANSLATIONAL IMPACTS
Awardee UEI
Y3B4DF5735W5
Awardee CAGE
96EQ5
Performance District
CA-28
Senators
Dianne Feinstein
Alejandro Padilla
Alejandro Padilla
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) | $275,000 | 100% |
Modified: 9/22/23