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

该项目的广泛影响/商业潜力影响到美国农业面临的最关键问题之一，可用劳动力短缺。这种短缺对水果和蔬菜行业的影响尤为明显，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，美国农产品供应商被迫严重依赖距离更远、质量较差的进口农产品。农业技术的进步已经极大地提高了农场在土地利用和水消耗方面的效率，使农产品能够在室内种植，使用高度复杂的商业温室，自动化养分输送和灯光控制。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，新开发的农业技术可以使这些任务自动化，通过提高国内农业经营的利润和效率，有可能产生重大的商业影响。自动化收割技术的商业应用也将使消费者受益，因为它使更高质量的产品更接近市场，并将美国农业部门定位为高技能技术工作的新增长领域。这个小企业创新研究（SBIR）第一阶段项目将专注于开发新的深度学习技术，用于使用计算机视觉摄像机识别可收获的水果（最初是西红柿），并准确估计它们的方向和连通性（由同一茎或藤连接的水果串）。一种能够实时运行的方法的成功开发将解决大量的技术风险，这些风险阻碍了机器人自动化采收解决方案商业化的能力。这样的进步也将为更广泛的计算机视觉和机器人操作社区提供新的见解，以应对稀疏和可变形的植物所面临的独特挑战。传统的目标姿态估计和抓取方法存在类似的结构。在这个第一阶段项目的后期，Root AI将把这些传感番茄果实方向的新方法整合到一个改进的运动和任务规划系统中，该系统使用额外的信息来智能地规划复杂的运动路径，在拥挤和严重闭塞的自然生长环境中收获单个水果。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 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.