RI: Medium: Robust Models and Physical Interactions for Managing Specialty Crops

这笔赠款支持研究开发能够可靠而有力地与植物互动的机器人，以帮助管理和收获特种作物。管理特殊作物，如水果和坚果，需要相当多的灵巧和技能。为了最大限度地提高产量，这些作物需要在收获前一年四季定期监测和修剪。这些任务是劳动密集型的，不适合通过传统的机械化方法实现自动化，这些方法用于玉米和大豆等大面积作物。因此，日益严重的劳动力短缺正在威胁着美国的特种作物行业。智能自动化为解决这一问题提供了一种很有前途的方法，机器人可以执行令人不适和危险的农活。然而，在非结构化环境中快速可靠地执行如此复杂的任务超出了当前机器人的能力。在这个项目中，一组研究人员将为机器人开发一个框架，以可靠的方式对特种作物进行建模和操作。新的感知算法将允许机器人使用视觉和触觉来识别植物的不同部分及其联系。新的控制器和规划算法将允许机器人深入植物的树冠，可靠地修剪、推开或收获植物的特定部分。所开发的方法不仅将为特种作物的自动化耕作提供支持，还将为创建更准确的这些植物模型以进行长期监测和表型鉴定提供技术支持。研究团队将通过在建模和操作灵活对象方面推进最先进的技术来解决管理和收获特种作物的挑战。研究人员将开发感知和多层建模技术，以捕捉场景的3D几何和物理属性。生成的模型将捕捉场景中的物理连接，并对这些高遮挡环境的不确定性进行建模。该团队将创建算法，以规划和执行与杂乱和受限环境的安全交互。研究还将包括开发互动感知方法，以改进基于与特产作物互动的经验的场景模型。在感知、规划和建模方面的研究贡献都将在实验室和现场的真实机器人上进行广泛的评估。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This grant supports research into developing robots that can reliably and robustly interact with plants to assist in the managing and harvesting of specialty crops. Managing specialty crops, such as fruits and nuts, requires a considerable amount of dexterity and skill. To maximize yields, these crops need to be monitored and pruned regularly throughout the year before being harvested. These tasks are labor intensive and not amenable to automation via the traditional mechanization methods used for broadacre crops such as corn and soybeans. An increasing labor shortage is thus threatening the US specialty crop industry. Intelligent automation presents a promising approach to addressing this issue, with robots performing the uncomfortable and dangerous farm work. However, performing such complex tasks quickly and reliably in unstructured environments is beyond the capabilities of current robots. In this project, a team of researchers will develop a framework for robots to reliably model and manipulate specialty crops in a robust manner. New perception algorithms will allow robots to use vision and touch to identify the different parts of the plants and their connections. New controllers and planning algorithms will allow robots to reach deep into the canopies of plants to reliably prune, push aside, or harvest specific parts of the plants. The developed methods will not only provide support for automating the farming of specialty crops, but also techniques for creating more accurate models of these plants for long-term monitoring and phenotyping. The team of researchers will address the challenges of managing and harvesting specialty crops by advancing the state of the art in modeling and manipulating flexible objects. The researchers will develop perception and multi-layer modeling techniques to capture the scenes’ 3D geometry and physical properties. The resulting models will capture the physical connections within the scenes as well as model the uncertainty for these high-occlusion environments. The team will create algorithms for planning and executing safe interactions with the cluttered and constrained environments. The research will also include the development of interactive perception methods for improving the scene models based on experiences from interacting with the specialty crops. Research contributions to perception, planning, and modeling will all be extensively evaluated on real robots both in the lab and in the field.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.