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

这项小企业创新研究（SBIR）第一阶段项目的更广泛/商业影响是使草莓的自动化收获更加高效和有效，因此，种植者采用的经济上更可行。目前在传统草莓农场部署的自动收割机无法可靠地处理草莓隐藏在厚厚的植物树冠下的旺季条件，而且必须将植物移开以查看和采摘果实。该项目将开发软件来扩展自动化可以提高生产力的条件集。草莓是美国第二大最受欢迎的水果，由于其高接触式收获过程，每英亩收获成本最高。收获过程的自动化减少了劳动力需求，有可能降低成本和/或提高水果的质量。此外，该项目预计将为美国工人创造高技能工作岗位。该项目的主要技术目标是提高草莓和植物的地图表示的质量和覆盖范围，从而增加草莓的收获数量和采摘率。第一阶段活动的范围是实施两种软件功能，并在模拟、实验室和现场环境中进行测试。第一个功能是安装在机器人操纵臂上的摄像机的轨迹优化模块。该技术旨在最大限度地提高信息增益，减少草莓的定位不确定性，同时尊重机械臂运动的运动学和碰撞约束。成功是通过相对于naïve预先计算的扫描的信息增益率来衡量的。第二个是一个训练有素的神经网络，它估计的参数，最好地定义一个操作任务计划，以取代叶片，以最大限度地提高草莓的可见度和访问，为后续采摘。训练和推理将以端到端方式完成，允许从场景的颜色和深度相机观察中估计给定任务计划的价值。这与传统的管道形成对比，传统管道不能利用神经网络来充分利用丰富的潜在表示。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.