Industry 4.0 has underscored the importance of human–robot collaboration (HRC), necessitating an efficient integration of human workers and robots to achieve high-productivity manufacturing. Traditional HRC-related teaching operations rely on intuitive tools, such as a teach pendant, but are effort-intensive and require personnel with specialized skills, particularly those who use collaborative robots in manufacturing. Thus, end-effector haptic devices that offer real-time tactile feedback and easy manipulation are being explored to address these issues. However, such devices have limitations in capturing the experiential movement of users and user-friendly haptic devices that are intuitive and convenient to operate are required. Recently, the integration of HRC and digital twins for human-involved manufacturing processes is being studied. Digital twin technology is used to seamlessly connect the physical and virtual domains using virtual models for monitoring the production process and enhancing the accuracy of operational process reconfiguration. However, most teaching devices that are interfaced with digital twins in manufacturing process still demand personnel with specialized training for their operation. To address these challenges, a novel framework is proposed herein that links an exoskeleton-type robotic system with the digital twin of a collaborative robot in manufacturing processes, effectively expediting robotic task instructions. The interactions between human users and digital twins, and that between digital twins and a collaborative robot, considerably enhance our understanding of human involvement in manufacturing processes and the execution of tasks by collaborative robots. This framework comprises three subsystems: a human operator outfitted with an exoskeleton-type robot and a virtual reality (VR) device, a digital twin, and a collaborative robot. The human operator interacts with the virtual robot within the digital twin via the VR device and exoskeleton robot, whereas the collaborative robot executes the given task and transmits the measured sensor information into the digital twin. Robot tracking in the experiment and usability study of a pick-and-place process performed on the proposed framework indicates that the proposed system enhances the ease of learning and intuitiveness to human operators than the traditional teaching methods in manufacturing processes.

中文翻译：

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将外骨骼机器人系统集成到工业制造应用的数字孪生中


工业 4.0 强调了人机协作 (HRC) 的重要性，需要人类工人和机器人的有效集成，以实现高生产率的制造。传统的HRC相关教学操作依赖于直观的工具，例如示教器，但工作量大，并且需要具有专业技能的人员，特别是在制造中使用协作机器人的人员。因此，正在探索提供实时触觉反馈和易于操作的末端执行器触觉设备来解决这些问题。然而，此类设备在捕捉用户的体验运动方面存在局限性，因此需要直观且操作方便的用户友好型触觉设备。最近，正在研究将 HRC 和数字孪生集成用于涉及人类的制造过程。数字孪生技术用于使用虚拟模型无缝连接物理域和虚拟域，以监控生产过程并提高操作流程重新配置的准确性。然而，大多数在制造过程中与数字孪生接口的教学设备仍然需要经过专门培训的人员进行操作。为了应对这些挑战，本文提出了一种新颖的框架，该框架将外骨骼型机器人系统与制造过程中协作机器人的数字孪生连接起来，有效地加快机器人任务指令。人类用户与数字孪生之间以及数字孪生与协作机器人之间的交互极大地增强了我们对人类参与制造过程以及协作机器人执行任务的理解。 该框架由三个子系统组成：配备外骨骼型机器人和虚拟现实（VR）设备的人类操作员、数字双胞胎和协作机器人。人类操作员通过 VR 设备和外骨骼机器人与数字孪生中的虚拟机器人进行交互，而协作机器人则执行给定的任务并将测量到的传感器信息传输到数字孪生中。在所提出的框架上进行的拾放过程的实验和可用性研究中的机器人跟踪表明，与制造过程中的传统教学方法相比，所提出的系统增强了操作员的学习便利性和直观性。