Singh Maulshree, Kapukotuwa Jayasekara, Gouveia Eber Lawrence Souza, Fuenmayor Evert, Qiao Yuansong, Murry Niall, Devine Declan
Polymer, Recycling, Industrial, Sustainability and Manufacturing Research Institute, Athlone Campus, Technological University of Shannon: Midland and Midwest, N37 HD68 Athlone, Ireland.
Software Research Institute, Athlone Campus, Technological University of Shannon: Midland and Midwest, N37 HD68 Athlone, Ireland.
Sensors (Basel). 2024 Aug 31;24(17):5680. doi: 10.3390/s24175680.
A digital twin (DT) is a virtual/digital model of any physical object (physical twin), interconnected through data exchange. In the context of Industry 4.0, DTs are integral to intelligent automation driving innovation at scale by providing significant improvements in precision, flexibility, and real-time responsiveness. A critical challenge in developing DTs is achieving a model that reflects real-time conditions with precision and flexibility. This paper focuses on evaluating latency and accuracy, key metrics for assessing the efficacy of a DT, which often hinder scalability and adaptability in robotic applications. This article presents a comprehensive framework for developing DTs using Unity and Robot Operating System (ROS) as the main layers of digitalization and communication. The MoveIt package was used for motion planning and execution for the robotic arm, showcasing the framework's versatility independent of proprietary constraints. Leveraging the versatility and open-source nature of these tools, the framework ensures interoperability, adaptability, and scalability, crucial for modern smart manufacturing applications. Our approach was validated by conducting extensive accuracy and latency tests. We measured latency by timestamping messages exchanged between the physical and digital twin, achieving a latency of 77.67 ms. Accuracy was assessed by comparing the joint positions of the DT and the physical robotic arm over multiple cycles, resulting in an accuracy rate of 99.99%. The results highlight the potential of DTs in enhancing operational efficiency and decision-making in manufacturing environments.
数字孪生(DT)是通过数据交换相互连接的任何物理对象(物理孪生)的虚拟/数字模型。在工业4.0的背景下,数字孪生对于大规模推动创新的智能自动化至关重要,因为它能显著提高精度、灵活性和实时响应能力。开发数字孪生的一个关键挑战是要实现一个能够精确且灵活地反映实时状况的模型。本文着重评估延迟和准确性,这是评估数字孪生效能的关键指标,而它们往往会阻碍机器人应用中的可扩展性和适应性。本文提出了一个以Unity和机器人操作系统(ROS)作为数字化和通信主要层面来开发数字孪生的综合框架。MoveIt软件包用于机器人手臂的运动规划和执行,展示了该框架不受专有约束的通用性。利用这些工具的通用性和开源特性,该框架确保了互操作性、适应性和可扩展性,这对现代智能制造应用至关重要。我们的方法通过进行广泛的准确性和延迟测试得到了验证。我们通过对物理孪生和数字孪生之间交换的消息进行时间戳来测量延迟,实现了77.67毫秒的延迟。通过在多个周期内比较数字孪生和物理机器人手臂的关节位置来评估准确性,准确率达到了99.99%。结果凸显了数字孪生在提高制造环境中的运营效率和决策能力方面的潜力。