A Digital Twin Architecture for Automated Guided Vehicles using a Dockerized Private Cloud

摘要

A Digital Twin is a virtual replica of a physical asset that can be used to simulate, monitor, and control its behavior in real-time. It has been increasingly used to better understand what is produced versus what was designed to bridge the gap between design and execution and to verify complex cyber-physical systems and assess their operation, especially under hypothetical scenarios. In this paper, we propose a novel architecture and implementation of a digital twin for a popular automated guided vehicle (AGV), namely TurtleBot3, which is a robot kit with open-source software. The design and development of the AGV Digital Twin architecture are introduced. This includes simulating the environment using Gazebo, modeling the AGV using a functional mock-up unit (FMU), monitoring the integrated sensors, and actuating the behavior of the virtual AGV and its physical counterpart using a dockerized private cloud with the aid of Robot Operating System (ROS). Along with providing a reference prototype for interested researchers and practitioners, the performance of the proposed system based on dockerized private cloud is contrasted to AWS services in terms of data recording latency as well as the inference time of MobilenetV1 on different setups. The results show that the proposed architecture for Digital Twins can effectively improve the efficiency and flexibility of AGV operation and provide valuable insights for maintenance, optimization, and testing "what-if" scenarios, with an average mean difference percentage of 4.1% and 6.46% for the Digital Twin and Physical Twin speed and position similarity. The proposed architecture is also 9.15 and 3.15 times faster for object detection inference time when compared to private cloud and edge devices, respectively. Additionally, it outperforms the public cloud in data transmission with a 5.5 times speed up.