SBIR Phase II: Versatile Robot Hands for Warehouse Automation

该项目的更广泛影响/商业潜力影响到美国经济增长最快的部门之一。2015年，电子商务销售额占美国零售总额的7.4%，预计还将迅速增长。通用拣货系统的商业影响潜力很大，因为目前的手工劳动方法是配送中心的痛点；由于高缺勤率、高流动率和人为失误，人工拣选是不愉快的、昂贵的和低效的。这项技术的成功也将有助于提高美国在机器人行业的竞争力。在排名前20位的配送系统集成商中，目前只有三家位于美国。机器人技术将成为这一领域进步的关键驱动力，我们的核心产品能力是未来自动化配送系统的关键组成部分。除了仓储物流，我们的技术可以受益的应用包括：工业自动化和制造业的广泛应用；军事应用（例如，IED处置，其中机器人可以执行对人类执行危险的任务）；辅助医疗保健（例如，机器人必须足够顺从，以便在人类周围安全，同时成功地与未知环境进行交互）。这个小企业创新研究第二阶段项目将专注于开发最先进的拣货机器人系统及其部署，最初的目标是订单履行行业。迄今为止，机器人系统在货架或手提袋运输库存方面取得了重大进展。然而，目前还没有一个部署的系统可以执行从库存箱中挑选单个物品并将其放入箱子中等待运输的任务。在这个项目的第一阶段，RightHand Robotics开发了一个采摘系统，远远领先于机器人抓取的研究文献，采摘数万个以前看不见的物体，错误率低于0.1%。在第二阶段，该项目将专注于使用机器学习技术推进数据驱动的掌握计划细化的最新技术，并将开发利用公司？S先进的兼容夹具。这些改进将使平均取放时间缩短至6秒或更短，未被发现的放置失败率低于万分之一。

The broader impact/commercial potential of this project affects one of the fastest-growing sectors of the US economy. E-commerce sales in 2015 accounted for 7.4% of total U.S. retail and are expected to rapidly rise. The potential for the commercial impact of general each-picking systems is high, as current manual labor methods are pain points for distribution centers; human picking is unpleasant, expensive and inefficient due to high absenteeism, high turnover and human error. The success of the proposed technology will also contribute to American competitiveness in the robotics industry. Of the top 20 distribution system integrators, only three are currently based in the U.S. Robotics is going to be the key driver of progress in this area, where each-picking, our core product capability, is a key component of future automated distribution systems. Beyond warehousing logistics, applications that our technology can benefit include: broad applications of industrial automation and manufacturing; military applications (e.g., IED disposal, where robots can perform tasks that are dangerous for humans to perform); and assistive healthcare (e.g., where robots must be compliant enough to be safe around humans while interacting successfully with unknown environments).This Small Business Innovation Research Phase II project will focus on the development of a state-of-the-art each-picking robotic system and its deployment, initially targeted at the order fulfillment industry. To date, robotic systems have enabled significant progress on transporting inventory on shelves or in totes. However, there has not yet been a deployed system that can perform the task of picking individual items from inventory bins and placing them in boxes for shipment. During Phase I of this project, RightHand Robotics developed a picking system far in advance of the research literature on robotic grasping, picking tens of thousands of items previously unseen objects, with error rates of less than 0.1%. During Phase II, the project will focus on advancing the state of the art in data-driven refinement of grasp planning using machine learning techniques, and will develop methods for box-packing that exploit the company?s advanced compliant grippers. These improvements will result in an average pick-and-place time of 6 seconds or less and an undetected placement failure rate of fewer one in ten thousand.