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）。数字制造和设计创新研究所的首席技术官表示，“制造业产生的数据比任何其他经济部门都多”。然而，要实现这些好处，需要能够以有利于制造业明智决策的方式理解、建模和分析处理信息。机器人自动化的进步使得机器人能够与人类一起工作。如今，缺乏安全保证阻碍了人机共生。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。未来制造种子资助 (FMSG) 网络制造项目旨在通过为人机团队开发弹性和安全的协调，并通过开发可靠和强大的方法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种用于制造系统安全高效运行的集成理论。网络物理人机团队（CPHMT）。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。本次总体目标该项目旨在开发用于 CPHMT 弹性协调的安全方法，利用预测模型来估计可用于构建制造零件流程的高保真数学模型的安全指数。具体来说，该项目 1) 开发用于人类的弹性和安全协调算法。 -使用可扩展且计算高效的心理过程计算模型（例如确定人类意图）进行机器组合，2）开发有效、可靠且易于实施的方法来构建制造零件流程的高保真数学模型，这是执行所必需的任何严格的定量分析和3) 应用：在基于实验室的测试台上验证理论结果，并通过工业案例研究来实现。解决机器人控制问题的方法基于利用深度学习的进步，使用操作员运动模型和注意力来预测人体运动。，以及工作空间和可达性约束，然后在耦合动态运动模型中利用运动预测来设计更安全的机器人控制器。解决制造问题的方法基于对具有 CPHMT 的制造系统中出现的随机过程的分析，并设计一个随机过程。机器人运行安全指标作为一项成果，该项目展示了 CPHMT 方法的有效性，并为制造专业人员提供了使用 CPHMT 进行生产操作和控制系统的有效工具。该研究的成果为制造组织提供了一种新型自动化 -灵活的自动化，因此机器学习、人工智能可以快速适应制造不同的产品，为了实现其部署，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。，其中描述了 CPHMT 方法，并且这项研究增强了学生对机器学习、控制和制造的理解，以及解决综合 STEM 问题的能力。该项目得到了工程土木、机械和制造创新部门 (CMMI) 的共同资助。 (ENG) 理事会、数学和物理科学理事会 (MPS) 的数学科学部 (DMS) 以及社会、行为和经济科学理事会的多学科活动办公室 (SMA) (SBE)。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。