SBIR Phase I: Real-Time Pose and Grasping Affordance Estimation for Vine Crops

该项目更广泛的影响/商业潜力影响了美国农业面临的最关键问题之一，即可用劳动力短缺。 这种短缺对水果和蔬菜产业的影响尤其明显，即使是短暂的劳动力损失也会导致可收获产品的全部损失。 最近，美国的农产品供应商被迫严重依赖从更远的地方采购的质量较低的进口农产品。 农业技术的进步已经大大提高了农场在土地利用和水消耗方面的效率，使农产品能够使用高度复杂的商业温室、自动化营养输送和光照控制在室内种植。 然而，到目前为止，这些商业温室仍然缺乏自动化常规收获，修剪和作物护理劳动任务的综合解决方案。 因此，能够使这些任务自动化的新开发的农业技术通过使国内农业经营更有利可图和更有效，具有巨大的商业影响的潜力。 自动化收获技术的商业应用也将使更高质量的农产品更接近市场，使美国农业部门成为高技能技术工作的新增长领域，从而使消费者受益。这个小企业创新研究（SBIR）第一阶段项目将专注于开发用于识别可收获水果的新深度学习技术。使用计算机视觉摄像机来识别（最初是西红柿），并准确地估计它们的方向和连接性（由同一茎或藤连接的一串水果）。 成功开发能够实时运行的方法将解决抑制机器人自动化收获解决方案商业化能力的重大技术风险。 这些进步还将为更广泛的计算机视觉和机器人操作社区带来新的见解，以应对稀疏和可变形的独特挑战。vine？类似于对象姿态估计和抓取的传统方法的结构。 在第一阶段项目的后期，Root AI将把这些感知番茄果实方向的新方法融入到改进的运动和任务规划系统中，该系统使用额外的信息智能地规划复杂的运动路径，以便在拥挤和严重闭塞的自然生长环境中收获单个果实。该奖项反映了NSF的法定使命，并通过使用基金会的学术价值和更广泛的影响审查标准。

The broader impact/commercial potential of this project affects one of the most critical problems facing the United States agricultural industry, a shortage of available labor. This shortage has had a particularly pronounced effect on the fruit and vegetable industry, where even a brief loss of labor can result in a total loss of harvestable products. More recently, U.S. produce suppliers have been forced to rely heavily on imported produce sourced from greater distances and of lower quality. Advancements in agricultural technology have already dramatically improved the efficiency of produce farms in terms of land utilization and water consumption by enabling produce to be grown indoors using highly sophisticated commercial greenhouses, automated nutrient delivery, and light control. However, to date, these commercial greenhouses still lack a comprehensive solution for automating routine harvesting, pruning, and crop care labor tasks. Thus, newly developed agricultural technologies which can automate these tasks have the potential for substantial commercial impact by making domestic farming operations more profitable and efficient. Commercial adoption of automated harvesting technology will also benefit consumers by enabling higher quality produce grown closer to market and position the U.S. agricultural sector as a new area of growth for highly skilled technical jobs.This Small Business Innovation Research (SBIR) Phase I project will focus on the development of new deep learning techniques used to identify harvestable fruits (initially tomatoes) using computer vision cameras and to accurately estimate their orientation and connectivity (bunches of fruits that are connected by the same stem or vine). Successful development of a method capable of running in real-time would resolve substantial technical risks which inhibit the ability to commercialize robotic automated harvesting solutions. Such advancements would also contribute newfound insights to the broader computer vision and robotic manipulation communities into the unique challenges that sparse and deformable ?vine? like structures present to traditional methods of object pose estimation and grasping. In the later portion of this Phase I project, Root AI will incorporate these new methods of sensing tomato fruit orientation into an improved motion and task planning system which uses the additional information to intelligently plan a complex movement path to harvest individual fruits in congested and heavily occluded natural growing environments.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.