A Deployable and Scalable ROS-Docker Framework for Multi-Platform Digital Twin Applications
Yehor Karpichev, Mahmoud Chick Zaouali, Todd Charter, Homayoun Najjaran · 2025
Digital twins are transforming robotics by enabling real-time monitoring, simulation, and control of physical systems. However, designing an efficient and scalable pipeline for synchronizing cyberphysical systems remains a challenge. This work presents a ROS-Docker-based framework for deploying digital twins, demonstrated with a collaborative robotic arm in a research lab setting. The proposed approach establishes a real-time connection between the physical robot and its digital counterpart while leveraging Docker containerization to enhance portability and reproducibility across different systems. The modularity of the approach easily extends support to multiple devices and platforms, laying a foundation for a flexible and scalable basis for robot control, automation, and AI-driven learning. This work paves the way for further research and the efficient deployment of digital twin applications in areas such as multi-robot systems, human-robot interaction, and autonomous decision-making.