## 1 Introduction  
The Digital twin (DT) has emerged as a transformative technology within the landscape of smart manufacturing and Industry 4.0. Its capability to seamlessly integrate virtual and physical environments has positioned digital twins as recognition a key innovation, garnering significance in both academic research and industrial applications. Since its inception, the DT concept has enabled numerous successful implementations across diverse industries, including product design, production optimization, predictive maintenance, and operational management [1]. Despite the increasing adoption of digital twin technology, a limited number of comprehensive reviews exist that focus specifically on its industrial applications [2, 3]. This gap highlights the need for further exploration and analysis of DT's role in enhancing industrial processes. The effectiveness of digital twins in industrial settings has been greatly amplified by advancements in complementary technologies. Innovations in augmented reality (AR) and virtual reality (VR) facilitate intuitive visualization and enable more natural user interaction with virtual models. Furthermore, the widespread adoption of smart sensors and the deployment of 5G networks have significantly improved real-time data collection and transmission capabilities. This real-time synchronization between virtual and physical systems is essential for maintaining the accuracy and efficiency of digital twin models [4]. Additionally, the integration of artificial intelligence (AI) and deep learning algorithms is expanding the predictive and optimization functionalities of digital twins. These advancements empower industries to anticipate equipment failures, optimize production processes, and make data-driven decisions in complex industrial environments. This convergence of cutting-edge technologies has fostered the development of highly adaptable and efficient smart factories, where digital twins enable the seamless integration, monitoring, and coordination of interconnected production systems [3; 5; 6]. The concept of the digital twin (DT) has gained significant attention in recent years as a powerful tool for the design, control, and optimization of robotic systems. Originally proposed by [PERSON] in 2003, the digital twin has rapidly evolved, finding applications across multiple fields of engineering and manufacturing [7; 8]. In the field of robotics, digital twins have proven to be versatile and invaluable tools. [PERSON] et al. [9] reviewed AI-driven digital twin applications in the industry, emphasizing their potential to enhance the efficiency and precision of robotic systems and manufacturing processes. In the context of production, digital twins enable the simulation of multiple scenarios and the optimization of resource allocation, leading to notable improvements in productivity and cost reduction [10; 11]. Aligned with these advancements, the integration of digital twins with robotic operating systems like the ROS (Robot Operating System) is transforming robotic simulations and real-world implementations [12; 13; 14]. The ROS offers a flexible and robust platform for robot simulation and control, facilitating seamless interaction between digital twins and physical systems [13]. This integration not only enables the simulation of robotic operations but also supports real-time synchronization and continuous feedback, enhancing the adaptability and efficiency of robotic systems [15]. For instance, [PERSON] et al. [16] developed a digital twin of a 5-degree-of-freedom industrial robotic manipulator using a miniaturized, additively manufactured model. The system was integrated with IoT sensors such as accelerometers and rotary encoders, as well as a ROS and MATLAB 2021(r), enabling the simulation of pick-and-place operations and state monitoring of the robotic arm. Their architecture, which involved real-time data collection and remote control, allowed for the performance monitoring and analysis of failure mechanisms, showcasing the predictive capabilities of digital twins.