SBIR Phase I: Automated Perception for Robotic Chopsticks Manipulating Small and Large Objects in Constrained Spaces

这个小型企业创新研究(SBIR)第一阶段项目的更广泛的影响/商业潜力将使用机器人来机械化物流链中最后剩下的完全人工任务之一：卸载包裹。甚至在新冠肺炎之前，运输业的增长就已经给在运输集装箱和配送中心之间运送包裹的劳动力带来了严重的压力。愿意并能够单调地一次将沉重的包裹搬到几英尺远的非熟练劳工群体太小了。新冠肺炎让这些供应链的重要性可见一斑。机械化将既加快供应链的速度，又有助于保护供应链免受传染病和其他自然灾害的影响。在解决物流业价值320亿美元的问题的同时，该项目将同时提高对几何建模的理解，以便更准确地感知和机器人操纵表现出显著几何变化的对象。这个小型企业创新研究(SBIR)第一阶段项目旨在建立针对机器人操纵应用的机器视觉算法，以便通过可用数字元组参数化的精选几何模型来识别RGB-D图像(红、绿、蓝色图像及其对应的深度图像的组合)中的对象。该技术还将估计识别对象的状态和参数。现有机器视觉方法中基于模型的对象识别和状态估计的最新技术使用固定模型，即具有恒定大小、形状、颜色和纹理的对象的模型。这些方法展示了识别数十个不同物体并在太空中定位它们的能力。但它们只对一些特定的图像起作用。该项目将在多个物体类别的相同识别和定位任务上追求相当的准确性，足以在供应链中对包裹进行机器人操作。研究目标规定了在场景中检测对象、估计精度和计算运行时性能(以每秒处理的帧为单位)所需的精确度和召回率的最低性能数字。预期的技术成果是一个能够与人类合作的半自动系统，它能够加快物体识别和参数识别，并提供超过全人工系统的估计。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。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will use robots to mechanize one of the last remaining fully manual tasks in logistics chains: unloading packages. Even before COVID-19, shipping growth was severely straining the human workforce that moves packages between shipping containers and distribution centers. The pool of unskilled laborers willing and able to monotonously move heavy packages a few feet at a time is much too small. COVID-19 made the importance of these supply chains evident. Mechanization will both speed the supply chain and help safeguard it from infectious diseases and other natural disasters. While addressing a $32 billion year problem in the logistics industry, the project will simultaneously enhance understanding of geometric modeling for more accurate sensing and robotic manipulation of objects that exhibit significant geometric variation.This Small Business Innovation Research (SBIR) Phase I project seeks to establish machine vision algorithms targeted to robotic manipulation applications in order to recognize objects from RGB-D images (a combination of red, green blue images and its corresponding depth image) via curated geometric models parameterizable with tuples of numbers. The technology will also estimate the state and parameters of recognized objects. The state-of-the-art in existing machine vision approaches to model-based object recognition and state estimation use fixed models, i.e., models of objects with constant size, shape, color, and texture. Those approaches have demonstrated the ability to identify scores of different objects and localize them in space. But they work on only some specific images. This project will pursue comparable accuracy, sufficient for robotic manipulation of packages in supply chains, on the same identification and localization tasks for multiple object categories. Research objectives specify the minimum performance numbers for the precision and recall required to detect objects in a scene, to estimate accuracy, and to calculate runtime performance (in frames processed per second). The anticipated technical result is a semi-autonomous system that is able to work with a human to speed object recognition and parameter identification, and to provide estimations beyond a fully manual system.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.