PFI:BIC: Smart Factories -An Intelligent Material Delivery System to Improve Human-Robot Workflow and Productivity in Assembly Manufacturing

PFI：BIC：智能工厂 - 智能物料输送系统，可改善装配制造中的人机工作流程和生产力

• 批准号: 1724982
• 负责人: Laurel Riek
• 金额: $ 100万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-15 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1724982&HistoricalAwards=false
• 关键词: PFI; BIC; Smart; Factories; Intelligent

Manufacturing represents a quarter of all employment in the US. To reshore jobs, improve operations, and recruit, retain, and retrain skilled workers, companies are increasingly using robotics technology. Ideally, robots will not replace humans but team with them to improve productivity. However, most industrial robots are poorly integrated into human workflow, causing expensive work stoppage problems ($1.7M per hour), worker stress, and talent loss. The research goal of this project is to address this problem by designing novel methods to improve human-robot workflow and productivity in assembly manufacturing through the use of an intelligent material delivery system (IMDS), which will closely integrate with and support the manual work process. This project will investigate innovative, multi-disciplinary approaches to this research area, dramatically advancing the state-of-the art in smart manufacturing and human-centered robotics. The research team will make the following contributions to human-centered smart service systems: 1) Revolutionize the use of robotics in assembly manufacturing processes to closely support skilled human workers, enabling them to focus on tasks they value (their trade) as opposed to tasks that distract from their talent (material movement). 2) Dramatically improve the productivity and flexibility of factories through nimble, real-time scheduling of an IMDS system that dynamically incorporates real-time models of human workflow. 3) Deeply explore the socio-technical implications of having an IMDS system in the workplace, in terms of human workers' cognition, fatigue, affect, and job satisfaction. The project's approach serves as a direct contrast to industry state-of-the-art, which relies on strict bifurcation of human and robot work, and rigid delivery schedules that fail to take local trim-line variations into account. By closely integrating an IMDS into the manual work process and understanding worker and material status, the project will readily enable flexibility and reconfigurability of a human workforce, an absolute necessity in made-to-order, small-batch manufacturing settings. This project will help the US manufacturing sector dramatically improve their operations by using automation to directly support a talented, skilled workforce. It has the potential to impact all major US manufacturing sectors, including automotive, construction, healthcare, energy, and goods. It will help US companies reshore operations, as well as create new opportunities for US worker STEM skill acquisition. Furthermore, this project involves a detailed investigation of multiple human worker implications of the transition from traditional to intelligent material delivery using robotics. By understanding reactions to such change, there will be new understanding on how to optimize a system not only for workflow and task efficiency but also for the human experience. Such knowledge is critical to maintaining job satisfaction, safety and health, and long-term well-being of the human workforce.The lead institution is the University of Notre Dame, Department of Computer Science and Engineering, in collaboration with the Massachusetts Institute of Technology, Department of Aerospace and Aeronautics (Cambridge, MA) and University of Colorado at Boulder, Department of Civil, Environmental, and Architectural Engineering (Boulder, CA). The primary industrial partner is Steelcase, Inc. (Grand Rapids, MI), a large manufacturer that specializes in customizable, made-to-order furniture.This proposal is co-funded by The Directorate for Computer and Information Science and Engineering (CISE), Divisions of Information and Intelligent Systems (IIS) and Computer and Network Systems (CNS)

制造业占美国所有就业人数的四分之一。为了重新安置工作，改善运营，招聘，留住和再培训技术工人，公司越来越多地使用机器人技术。理想情况下，机器人不会取代人类，而是与人类合作以提高生产力。然而，大多数工业机器人与人类工作流程的整合程度很差，导致了昂贵的停工问题（每小时170万美元）、工人压力和人才流失。 该项目的研究目标是通过设计新的方法来解决这个问题，通过使用智能材料交付系统（IMDS）来改善装配制造中的人机工作流程和生产率，该系统将与手动工作流程紧密结合并支持手动工作流程。该项目将研究这一研究领域的创新，多学科方法，大大推进智能制造和以人为本的机器人技术的发展。该研究团队将为以人为本的智能服务系统做出以下贡献：1）彻底改变机器人在装配制造过程中的使用，以密切支持熟练的人类工人，使他们能够专注于他们重视的任务（他们的贸易），而不是分散他们的才能的任务（材料移动）。2)通过IMDS系统的灵活、实时调度，大幅提高工厂的生产力和灵活性，该系统动态地结合了人类工作流程的实时模型。3)深入探讨在工作场所使用IMDS系统的社会技术影响，包括人类工人的认知、疲劳、情感和工作满意度。该项目的方法与行业最先进的方法形成了直接对比，后者依赖于人类和机器人工作的严格分歧，以及未能考虑局部修剪线变化的刚性交付时间表。通过将IMDS紧密集成到手动工作流程中并了解工人和材料状态，该项目将很容易实现人力资源的灵活性和可重新配置性，这在定制，小批量制造环境中是绝对必要的。该项目将帮助美国制造业通过使用自动化来直接支持有才华的熟练劳动力，从而大大改善其运营。它有可能影响美国所有主要制造业，包括汽车、建筑、医疗保健、能源和商品。它将帮助美国公司重新开展业务，并为美国工人获得STEM技能创造新的机会。此外，该项目还涉及到使用机器人技术从传统到智能材料交付过渡的多个人类工作者影响的详细调查。通过理解对这种变化的反应，将对如何优化系统有新的理解，不仅是为了工作流程和任务效率，而且是为了人类体验。这类知识对于保持工作满意度、安全和健康以及人类劳动力的长期福祉至关重要，牵头机构是圣母大学计算机科学与工程系，与马萨诸塞州理工学院航空航天系合作（剑桥，MA）和位于博尔德的科罗拉多大学土木、环境和建筑工程系（博尔德，CA）。主要工业合作伙伴是Steelcase，Inc.。（大急流城，密歇根州），一家大型制造商，专门从事定制，定制家具。这项建议是共同资助的董事会计算机和信息科学与工程（CISE），部门的信息和智能系统（IIS）和计算机和网络系统（CNS）

项目成果

Fluent Coordination in Proximate Human Robot Teaming

• 发表时间: 2019
• 作者: S. Matsumoto;L. Riek

Consider the Human Work Experience When Integrating Robotics in the Workplace

在工作场所集成机器人技术时考虑人类的工作体验

• DOI: 10.1109/hri.2019.8673139
• 发表时间: 2019
• 期刊: 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI
• 作者: Welfare, Katherine S.;Hallowell, Matthew R.;Shah, Julie A.;Riek, Laurel D.
• 通讯作者: Riek, Laurel D.

Activity recognition in manufacturing: The roles of motion capture and sEMG+inertial wearables in detecting fine vs. gross motion

• DOI: 10.1109/icra.2019.8793954
• 发表时间: 2019-05
• 期刊: 2019 International Conference on Robotics and Automation (ICRA)
• 作者: A. Kubota;T. Iqbal;J. Shah;L. Riek

Socially intelligent task and motion planning for human-robot interaction

用于人机交互的社交智能任务和运动规划

• 发表时间: 2019
• 期刊: and Systems (RSS
• 作者: Frank, A;Riek, L.D.
• 通讯作者: Riek, L.D.

Trust-aware control in proximate human-robot teaming

近距离人机协作中的信任感知控制

• DOI: 10.1016/b978-0-12-819472-0.00015-0
• 发表时间: 2020
• 期刊: Trust in Human Robot Interaction
• 作者: Washburn, A;Matsumoto, S.;Riek, L. D.
• 通讯作者: Riek, L. D.