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SBIR Phase I:Trajectory Optimizations and Learned Foliage Manipulation to Accelerate Throughput in Automated Strawberry Harvesting

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 2322402
Agency Tracking Number: 2322402
Amount: $275,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: R
Solicitation Number: NSF 23-515
Solicitation Year: 2023
Award Year: 2023
Award Start Date (Proposal Award Date): 2023-09-15
Award End Date (Contract End Date): 2024-02-29
Small Business Information
4802 La Canada Blvd
La Canada Flintridge, CA 91011
United States
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Alexander Gutierrez
 (323) 972-7470
Business Contact
 Alexander Gutierrez
Phone: (323) 972-7470
Research Institution

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_
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_
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.

* Information listed above is at the time of submission. *

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