PFI:AIR - TT: High-Reliability Robot Grasping for Per-Item Distribution

PFI:AIR - TT：用于按件分配的高可靠性机器人抓取

• 批准号: 1500178
• 负责人: Robert Howe
• 金额: $ 20万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-01 至 2017-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1500178&HistoricalAwards=false
• 关键词: PFI; AIR; TT; Reliability; Robot

This PFI:AIR Technology Translation project is aimed at a proof-of-concept demonstration of a robotic grasping system with an error rate of 1 in 10,000 trials or better. In prior NSF-funded work, the researchers developed highly capable, low-cost robot hands which, when paired with a standard industrial robot arm and vision system, can grasp a sufficiently wide range of items to be useful for e-commerce order fulfillment and automated restocking applications. The intended applications, however, require extremely low error rates. This project will (1) develop a system to independently test the reliability of the grasping system on a realistic variety of potential customer inventory, and (2) implement and evaluate new mechanisms for the system to detect and correct errors during grasping.The proposed in-depth evaluation of the reliability of grasping systems in real-world tasks is unprecedented. To date, published assessments of grasp system success is limited to a few dozen to a few hundred trials - and typically results in error rates of 1-10%. The proposed methods for evaluation of grasp errors represents an essential capability for validating the performance of robot hands as they move out of laboratories and factories into diverse real-world settings like homes, hospitals, and shop floors. In addition, this project will develop new methods for independently assessing errors in grasping, as well as new methods for enhancing the reliability of grasping. The proposed system will produce very large data sets that will be exploited in future work for data-driven learning of grasp control and on-line error detection and correction. In addition, post docs involved in this project will receive entrepreneurship and technology translation experiences through working to define customer needs and developing application-driven technology.High-reliability automated grasping systems promise to reduce costs and enhance the productivity of the warehousing and logistics businesses that are a rapidly-growing segment of the economy. By automating the selection of inventory from automated storage and retrieval systems, tasks such as order picking, auditing and packing can be accomplished with fewer sources of error, which is crucial in handling high-value products such as pharmaceuticals and electronics. The development of systems and methods for assessing grasping success for diverse object sets will lay the essential groundwork for many imminent real-world applications of grasping in less structured environments.

这个PFI：AIR技术翻译项目旨在对机器人抓取系统进行概念验证，其错误率为1/10，000或更低。在之前NSF资助的工作中，研究人员开发了高性能，低成本的机器人手，当与标准工业机器人手臂和视觉系统配对时，可以抓取足够广泛的物品，用于电子商务订单履行和自动补货应用。然而，预期的应用需要极低的错误率。该项目将（1）开发一个系统，以独立测试抓取系统在各种现实的潜在客户库存上的可靠性，（2）实施和评估系统在抓取过程中检测和纠正错误的新机制。到目前为止，已发表的抓取系统成功评估仅限于几十到几百次试验-通常导致1- 10%的错误率。所提出的评估抓取误差的方法代表了验证机器人手的性能的基本能力，因为它们从实验室和工厂进入家庭，医院和车间等各种现实环境。此外，本项目还将开发独立评估抓握错误的新方法，以及提高抓握可靠性的新方法。该系统将产生非常大的数据集，将在未来的工作中利用数据驱动的学习掌握控制和在线错误检测和校正。此外，参与该项目的博士后将通过明确客户需求和开发应用驱动技术，获得创业和技术翻译经验。高可靠性的自动抓取系统有望降低成本，提高经济中快速增长的仓储和物流业务的生产率。通过从自动化存储和检索系统中自动选择库存，可以在更少的错误来源下完成订单拣选、审计和包装等任务，这对于处理药品和电子产品等高价值产品至关重要。开发系统和方法来评估不同对象集的抓取成功率，将为许多即将在结构化程度较低的环境中进行抓取的实际应用奠定必要的基础。