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

SBIR 第一阶段：轨迹优化和学习叶子操纵，以提高自动化草莓收获的吞吐量

• 批准号: 2322402
• 负责人: Alexander Gutierrez
• 金额: $ 27.5万
• 依托单位: L5 AUTOMATION INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2322402&HistoricalAwards=false
• 关键词: SBIR; Phase; Trajectory; Optimizations; Learned

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 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.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.

这项小型企业创新研究（SBIR）I阶段项目的广播/商业影响是使草莓的自动收获更加高效，有效，因此对增长的采用更为可行。当草莓隐藏在厚植物的冠层下方，必须放置植物以查看和挑选水果时，目前部署在常规草莓农场中的自动收割机无法可靠地处理峰值季节。该项目将开发软件，以扩大一组条件，从而可以提高生产率。草莓是美国第二受欢迎的水果，由于其高触摸收获过程，每英亩的成本最高。自动化收获过程可减少劳动力需求，从而有可能降低成本和 /或提高水果的质量。此外，该项目有望为美国工人创造高技能工作。该项目的主要技术目的是提高草莓和植物地图表示的质量和覆盖范围，这将增加收获的草莓的数量以及采摘的速度。 I阶段活动的范围是实现两个软件功能，并在模拟，实验室和现场环境中测试它们。第一个功能是安装在机器人操纵器上的相机的轨迹优化模块。该技术将旨在最大程度地提高信息增益并减少草莓的定位不确定性，同时尊重机器人组运动的运动学和碰撞约束。成功将通过相对于幼稚的预定扫描的信息增益率来衡量。第二个是一个受过训练的神经元网络，该网络估算了最能定义操纵任务计划的参数，该计划可以取代叶子，以最大程度地提高草莓的可见性和随后的选择的访问。培训和推论将以端到端的方式进行，从而从颜色和深度摄像头观察中估算给定任务计划的价值。这与传统的管道形成鲜明对比，该管道与神经网络没有使大部分丰富的潜在表现成为可能。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被视为通过评估来获得支持。