SBIR Phase II: Versatile Robot Hands for Warehouse Automation

SBIR 第二阶段：用于仓库自动化的多功能机器人手

• 批准号: 1632460
• 负责人: Lael Odhner
• 金额: $ 75万
• 依托单位: RightHand Robotics, LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1632460&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Versatile; Robot

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.

该项目的更广泛影响/商业潜力影响到美国经济中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%，预计还会迅速上升。一般的每次挑选系统的商业影响的可能性很高，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、高周转率和人为错误，人工挑选令人不快、昂贵且效率低下。这项拟议技术的成功还将有助于提高美国在机器人行业的竞争力。在排名前20位的分销系统集成商中，目前只有三家总部位于美国。机器人将成为这一领域进步的关键驱动力，我们的核心产品能力--每次挑选--将是未来自动化分销系统的关键组件。除了仓储物流，我们的技术可以受益的应用包括：工业自动化和制造的广泛应用；军事应用(例如，IED处理，机器人可以执行对人类执行有危险的任务)；以及辅助医疗(例如，机器人必须具有足够的合规性，以便在与未知环境成功交互的同时，能够与人类安全相处)。这个小型企业创新研究第二阶段项目将专注于开发最先进的每次挑选机器人系统及其部署，最初目标是订单履行行业。到目前为止，机器人系统已经在货架或手提箱上运输库存方面取得了重大进展。然而，目前还没有一个已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以供装运的任务。在该项目的第一阶段，Right Hand Robotics开发了一种拾取系统，远远领先于机器人抓取的研究文献，拾取了数万件以前未见过的物体，错误率不到0.1%。在第二阶段，该项目将专注于利用机器学习技术推进数据驱动的抓取规划精细化方面的最新技术，并将开发利用该公司的盒子包装方法-S先进的顺应夹持器。这些改进将导致平均拾取和放置时间为6秒或更短，并且未检测到的放置失败率低于万分之一。