## 2 Methodology
### Search Strategy and Study Selection  
The search terms were carefully crafted to align with the research questions, aiming to strike a balance between broad retrieval and precise filtering to ensure the inclusion of the most relevant literature. Adjustments to search terms were made iteratively. Table 2 presents a summary of the keyword combinations and specific filters applied to each database. The search for relevant peer-reviewed journal articles was conducted in January 2025 using the IEEE Xplore, ACM Digital Library, SpringerLink, and ScienceDirect databases. Due to the high concentration of relevant articles initially found in the ScienceDirect database, a second round of searches was conducted. In this phase, the MDPI database, which was not included in the initial search, was also incorporated to improve coverage. This updated search process led to the identification of 226 additional candidate articles in ScienceDirect and 9 in MDPI, thereby enhancing the comprehensiveness of the review. Since the initial search string yielded only a small number of results in MDPI, an additional broader search was conducted. This expanded query returned 126 results. The search window begins in 2021, aligning with the formal introduction and growing academic focus on the Industry 5.0 paradigm [5]. The literature prior to 2021 was excluded to maintain alignment with the specific objectives of this review, which aims to capture recent trends, technologies, and use cases that reflect the shift from Industry 4.0 to Industry 5.0.  
The proposed inclusion and exclusion criteria are outlined in Table 3. The article selection process is illustrated in Figure 1. We screened the titles, abstracts, and keywords of the articles to identify those containing relevant terms related to robotics and simulation, in accordance with the proposed inclusion criteria. Subsequently, the exclusion criteria were applied after obtaining and reviewing the full articles to eliminate those that were out of scope or lacked sufficient technical details and quality. During the full-text review, a quality assessment was conducted based on the following questions:  
* Was the article published in a reputable, peer-reviewed journal (preferably indexed and with an impact factor)?
* Does the study present a clearly described system architecture or implementation?
* Is the research grounded in applied science and relevant to real-world applications?  
To mitigate potential biases, the screening process was conducted in multiple rounds by multiple reviewers. Any disagreements between reviewers were resolved through discussion.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Database** & **Search Terms and Filters** \\ \hline IEEE Xplore & _robot AND (simulation OR simulators OR “digital twin”) AND ROS AND industry._**Filters:** 2021–2025. \\ ScienceDirect (first search) & _robot AND (simulation OR simulators OR “digital twin”) AND ROS AND (industrial OR service OR assistive) AND human robot interaction._**Filters:** 2021–2025, Research Articles, Engineering. \\ ScienceDirect (second search) & _robot AND (simulation OR simulators OR “digital twin”) AND “Robot Operating System” AND (“industry 5.0” OR “industry 4.0”)._**Filters:** 2021–2025, Research Articles, Engineering. \\ SpringerLink & _robot AND (simulation OR simulators OR “digital twin”) AND ROS AND (“industry 5.0” OR “industry 4.0”)._**Filters:** Article, 2021–2025, English. \\ ACM Digital Library & _robot AND (simulation OR simulators OR “digital twin”) AND ROS AND (“industry 5.0” OR “industry 4.0”)._**Filters:** 2021–2025. \\ MDPI (first search) & _robot AND (simulation OR simulators OR “digital twin”) AND ROS AND (“industry 5.0” OR “industry 4.0”)._**Filters:** 2021–2025. \\ MDPI (second search) & _robot AND (simulation OR simulators OR “digital twin”) AND (“industry 5.0” OR “industry 4.0”)._**Filters:** 2021–2025. \\ \hline \end{tabular}
\end{table}
Table 2: Keywords and filters applied per database.