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FMSG: Cyber: Resilient and Reliable Cyber-Physical-Human-Machine Teams: Toward Future of Cybermanufacturing

FMSG：网络：有弹性且可靠的网络物理人机团队：迈向网络制造的未来

基本信息

批准号：
2134367
负责人：
Ashwin Dani
金额：
$ 50万
依托单位：
University of Connecticut
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-01-01 至 2024-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2134367&HistoricalAwards=false
关键词：
FMSG Cyber Resilient Reliable Physical

项目摘要

Industry 4.0 aims at converting traditional operator-controlled systems into smart cyber-physical systems (CPS). The chief technology officer of the Digital Manufacturing and Design Innovation Institute stated that “manufacturing generates more data than any other sector of the economy”. The competitive edge provided by the big data are widely acknowledged. However, achieving these benefits requires the ability to understand, model and analytically process information in a way that is conducive to informed decision-making in manufacturing. Advances in robotic automation has led to robots working alongside humans in manufacturing/assembly/repair facilities. Today, lack of safety assurance precludes human-robot symbiosis. Moreover, there is also a lack of high-fidelity modeling techniques that can effectively utilize the vast amount of data available from the sensors in manufacturing facilities. This Future Manufacturing Seed Grant (FMSG) CyberManufacturing project aims to create new science and develop new talent for the advancement of resilient and reliable human-CPS systems by developing resilient and safe coordination for human-machine teaming, and by developing reliable and robust methods for part flow models in manufacturing systems in the presence of external disturbances. We refer to these systems as cyber-physical human machine teams (CPHMT). The overarching goal of this project is to develop an integrated theory for safe and efficient operations of manufacturing systems with cyber-physical human machine teams (CPHMT).One of the main difficulties in deploying CPHMT is how to achieve resiliency of the human-machine teams and incorporate that information in the system level optimization for decision-making on the factory floor. Therefore, the overall goal of this project is to develop safety methods for resilient coordination of CPHMT, utilize predictive modeling to estimate safety index that can be used in construction of high-fidelity mathematical models of manufacturing parts flow. Specifically, the project 1) develops resilient and safe coordination algorithms for human-machine teaming using scalable and computationally efficient computational modeling of psychological processes such as determining human intentions, 2) develops effective, reliable, and easy-to-implement approach to construct high-fidelity mathematical models of manufacturing parts flow, which is necessary to perform any rigorous, quantitative analysis and optimization. 3) Application: Validate the theoretical results in lab bench-based testbed and implement them through industrial case studies. The approach to the robot control problems is based on utilizing advances in deep learning to predict the human motion using operator motion model, attention, and workspace and reachability constraints. Then the motion prediction is utilized in a coupled dynamic motion model to design safer controllers for robots. The approach to the manufacturing problems is based on analyses of random processes, which arise in manufacturing systems with CPHMT and designing a safety index for the robot to operate based on human motion intent. As an outcome, this project demonstrates the efficacy of the CPHMT approach and provide manufacturing professionals with effective tools for production operation and control of systems with CPHMT. The outcomes of this research provide manufacturing organizations with a novel type of automation - flexible automation, whereby the machine learning, artificial intelligence can be rapidly adapted to manufacture different products. To enable its deployment, modules related to machine learning, robotics and automation are developed to be offered as a part of newly approved Robotics engineering undergraduate major at UConn, where the CPHMT approach is described and illustrated. This study enhances the students' understanding of machine learning, control and manufacturing, and their capabilities to solve comprehensive STEM problems.This project is supported with co-funding from the Division of Civil, Mechanical and Manufacturing Innovation (CMMI) in the Engineering (ENG) Directorate, the Division of Mathematical Sciences (DMS) in the Directorate for Mathematical and Physical Sciences (MPS), and the Office of Multidisciplinary Activities (SMA) in the Directorate of Social, Behavioral and Economic Sciences (SBE).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.

工业4.0旨在将传统的操作员控制系统转变为智能网络物理系统（CPS）。数字制造与设计创新研究所（Digital Manufacturing and Design Innovation Institute）的首席技术官表示，“制造业产生的数据比任何其他经济部门都多”。大数据带来的竞争优势得到了广泛认可。然而，实现这些好处需要理解、建模和分析处理信息的能力，这有助于在制造过程中做出明智的决策。机器人自动化的进步导致机器人在制造/装配/维修设施中与人类一起工作。如今，缺乏安全保障阻碍了人机共生。此外，还缺乏能够有效利用制造设施中传感器提供的大量数据的高保真建模技术。该未来制造种子基金（FMSG）网络制造项目旨在通过开发人机团队的弹性和安全协调，以及通过开发存在外部干扰的制造系统中零件流动模型的可靠和稳健方法，为弹性和可靠的人- cps系统的进步创造新科学和培养新人才。我们将这些系统称为网络-物理人机团队（CPHMT）。该项目的总体目标是开发一个集成理论，用于与网络物理人机团队（CPHMT）一起安全高效地运行制造系统。部署CPHMT的主要困难之一是如何实现人机团队的弹性，并将该信息纳入工厂车间决策的系统级优化中。因此，本项目的总体目标是开发CPHMT弹性协调的安全方法，利用预测建模来估计安全指标，用于构建高保真的制造零件流数学模型。具体而言，该项目1)开发具有弹性和安全的人机协作协调算法，使用可扩展和计算效率高的心理过程计算建模（如确定人类意图）；2)开发有效、可靠和易于实现的方法来构建制造零件流的高保真数学模型，这对于执行任何严格的定量分析和优化都是必要的。3)应用：在实验室台架测试平台上验证理论结果，并通过工业案例研究实施。机器人控制问题的方法是基于利用深度学习的进展来预测人类运动，使用操作员运动模型，注意力，工作空间和可达性约束。然后将运动预测应用于耦合动态运动模型中，设计出更安全的机器人控制器。该方法基于对CPHMT制造系统中产生的随机过程的分析，并基于人的运动意图设计机器人操作的安全指标。因此，该项目证明了CPHMT方法的有效性，并为制造专业人员提供了使用CPHMT进行生产操作和系统控制的有效工具。本研究的结果为制造组织提供了一种新型的自动化-柔性自动化，即机器学习，人工智能可以快速适应制造不同的产品。为了使其能够部署，开发了与机器学习，机器人和自动化相关的模块，作为康涅狄格大学新批准的机器人工程本科专业的一部分，其中描述和说明了CPHMT方法。本课程提高学生对机器学习、控制和制造的理解，以及解决综合STEM问题的能力。该项目由工程（ENG）局的土木、机械和制造创新司（CMMI）、数学和物理科学局（MPS）的数学科学司（DMS）以及社会、行为和经济科学局（SBE）的多学科活动办公室（SMA）共同资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。