Development of a Digital Twin of an Industrial Manipulator Based on the Robot Operating System

This paper presents the development of a digital twin for an industrial robotic manipulator using the Robot Operating System and the Unified Robot Description Format. The digital twin integrates the manipulator's 3D model, kinematics, and dynamics, enabling precise simulation, motion planning, and control in a virtual environment. The key development steps include the creation of a Robot Operating System model, configuration of Robot Operating System controllers, integration with simulation tools such as Gazebo and RViz, and trajectory planning using Move It. The digital twin allows for comprehensive testing and optimization of control algorithms before deployment on physical hardware, reducing development risks and costs. The research addresses practical challenges, including controller configuration errors, trajectory mismatches, and model visualization discrepancies, ensuring high simulation accuracy. Computational experiments demonstrate the effectiveness of Robot Operating System in designing and testing robotic systems, highlighting its adaptability for various industrial applications. The developed digital twin serves as a foundation for advanced robotic control systems, enabling real-time monitoring, predictive maintenance, and adaptive control strategies. This work aligns with the principles of Industry 5.0, emphasizing human-centric, sustainable, and resilient industrial systems. By providing a virtual platform for testing and optimization, the digital twin enhances the efficiency and safety of robotic operations, facilitating human-machine collaboration. The results underscore the potential of digital twins in advancing smart manufacturing, accelerating innovation cycles, and improving system performance. This research contributes to the growing field of industrial robotics, offering a scalable and adaptable solution for modern manufacturing challenges. The research was carried out within the framework of the RSF grant No. 24-29-00645.