## 1 Introduction
Similarly, [PERSON] et al. [17] developed a ROS-based simulation environment where digital twins enhanced the flexibility of modern production systems. [PERSON] and [PERSON] [18] explored the integration of digital twins with Industry 4.0 technologies to create adaptive and collaborative manufacturing cells. Their study demonstrated how digital twins facilitate real-time simulation, dynamic task allocation based on worker skills, and trajectory planning for collaborative robots. This approach enables quick adaptation to changes in the production environment, improving the efficiency of human-robot collaboration in assembly systems. The applications of digital twins are not limited to robotics; they have also been applied to energy systems for real-time optimization. For example, in the Rinascimento III district in Rome, digital twins integrated with IoT, AI, and machine learning (ML) technologies are used to simulate and optimize energy systems. These models allow for accurate evaluations of intervention scenarios, improving energy generation and distribution efficiency while facilitating informed decision making [19; 20]. [PERSON] et al. [21] further highlight that data-driven digital twins developed in real-time, data-rich environments improve decision making and predictive capabilities, particularly in renewable energy technologies. In a robotic simulation, [PERSON] et al. (2018) proposed TuneNet, a model that fine-tunes simulator parameters to match real-world conditions. This method enables the seamless transfer of skills from simulation to reality (sim-to-real) in robotic systems. Similarly, [PERSON] et al. (2018) combined supervised learning with digital twins for anomaly detection in industrial systems. They generated training datasets by simulating the normal operations of a monitoring system then applied machine learning models such as Siamese autoencoders for detecting real-world anomalies. In the energy sector, [PERSON] and [PERSON] (2018) presented a parallel control scheme using the ACP method to enhance the operational flexibility of ultra-supercritical (USC) thermal power plants. Their artificial control system, built on digital twin technology, reflects real-time operating conditions and guides USC units through computational experiments, ensuring stable operations. Meanwhile, [PERSON] and [PERSON] (2018) introduced the Self-Optimizing Parallel Control (SOC) framework, combining parallel intelligence, digital twins, and the derivative-free Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm to enhance smart process control. Specifically in the domain of parallel manipulators, [PERSON] et al. (2018) proposed an advanced control method for a four-degree-of-freedom parallel robot. Their approach, based on modal decoupling and gravity-load compensation, significantly improved control precision and robust system stability. In the robotics field, [PERSON] (2018) explores human-robot collaboration in customized production using digital twins to enhance interaction and safety. However, challenges remain in developing accessible and effective systems for managing both physical and virtual environments. To address this, [PERSON] proposes a modular web-based system built on a client/server architecture, employing MySQL and the Microsoft NET Framework. The study includes a case of human-robot collaborative assembly, presenting findings that contribute to the standardization and effective management of digital twin applications in industrial settings. Similarly, [PERSON] (2018) introduces a digital twin prototype named \"Alita\", which improves robotic manipulation by implementing an open kinematic chain manipulator. The system supports task replanning and real-time human-robot control through a four-stage representation that integrates control dynamics. Two strategies are proposed: one based on a deep learning model for task replanning and another using hybrid mapping for collaborative control.