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全手动任务之一：卸载包裹。即使在COVID-19之前，航运业的增长也严重影响了在集装箱和配送中心之间运送包裹的人力。愿意并且能够单调地将沉重的包裹一次移动几英尺的非熟练工人数量太少了。COVID-19使这些供应链的重要性变得显而易见。机械化将加快供应链的速度，并有助于保护供应链免受传染病和其他自然灾害的影响。在解决物流业每年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，以便对具有显著几何变化的物体进行更精确的传感和机器人操作。该小型企业创新研究（SBIR）第一阶段项目旨在建立针对机器人操作应用的机器视觉算法，以便从RGB-D图像中识别物体（红色、绿色蓝色图像及其对应的深度图像的组合）。 该技术还将估计已识别物体的状态和参数。现有的基于模型的对象识别和状态估计的机器视觉方法中的最新技术使用固定模型，即，具有恒定大小、形状、颜色和纹理的对象模型。这些方法已证明有能力识别数十个不同的物体并在空间中确定它们的位置。但它们只对某些特定的图像起作用。该项目将追求可比的准确性，足以在供应链中对包装进行机器人操作，对多个对象类别执行相同的识别和定位任务。研究目标规定了检测场景中的对象、估计准确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半自主系统，能够与人类一起工作，以加快物体识别和参数识别，并提供超出完全手动系统的估计。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

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.