## 3 Review of Papers Dealing with the Application of Robots in Waste Separation  
### Selection of Papers  
The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) protocol was used for the preparation of the literature review of this paper [22]. The papers were collected from the Scopus and Web of Science databases. Both sources were searched separately. When searching for papers, the keywords \"e-waste\", \"robots\", and \"artificial intelligence\" were used in different combinations, with the help of Boolean operators \"AND\", \"OR\", and \"NOT\". Filters such as \"full-text only\" and \"English language\" were applied. Only papers published in a journal or at a conference were included. In the PRISMA diagram, the number of publications for each database is entered individually, as is the total number of papers obtained after applying all criteria in the left cell at the top of the diagram (Figure 1).  
Following the PRISMA protocol, we eliminated duplicate publications in the next step and collected the remaining publications from the Scopus and Web of Science databases. Next, we excluded papers that were not available (they were not open access nor did we have institutional access to them without an additional fee) and those that, upon reviewing the title and abstract, did not align with the research objective. After reading the remaining papers, out of the initial 412, only 10 papers were included in the literature review. In the conducted literature search, according to the given criteria, keywords could appear anywhere in a paper, including the literature review. This resulted in a significant number of irrelevant papers, which were excluded from the research. Table 1 shows insights from the selected papers.  
Figure 1: PRISMA flowchart showing final results [22].  
\begin{table}
\begin{tabular}{p{12.3 pt} p{128.0 pt} p{128.0 pt} p{128.0 pt}} \hline \hline
**Reference** &
\begin{tabular}{c} **Country of the** \\ **First Author** \\ \end{tabular} & \multicolumn{2}{c}{**Insights**} \\ \hline
1. [PERSON] et al., 2022 [23] & Japan & \(\bullet\) & The survey discusses difficulties in waste sorting automation for mixed industrial waste. \\  & 2022 [23] & \(\bullet\) & It provides an overview of robotic sorters’ challenges in grasping, sensing, and trajectory planning. \\ \hline
2. [PERSON] et al., 2010 [24] & Austria & \(\bullet\) & An ontology-based system integrates multi-agent machine control and vision-based, real-time path planning. \\  & 2010 [24] & It enables autonomous disassembly operation coordination with vision system coupling. \\ \hline
3. [PERSON], 2019 [25] & United Kingdom & \(\bullet\) & An overview of commercially available robotic systems for waste sorting and disassembly. \\  & 2019 [25] & AI-enhanced robotic systems are playing a growing role in the sorting of municipal and industrial waste, prior to recycling. \\  & 2022 & Delta-robot-based systems can achieve pick rates of 60–70 items/min. \\ \hline
4. [PERSON] and [PERSON], 2017 [9] & Spain & \(\bullet\) & The study emphasizes the importance of human–robot collaboration in WEEE recycling and process optimization. The integration of cobots leads to a higher degree of material recovery and separation, which results in better economic performance compared to traditional manual processes. \\ \hline
5. [PERSON] et al., 2022 [26] & Saudi Arabia & \(\bullet\) & This study reviewed 40 studies from 2019–2021 on deep learning in solid waste management. \\  & 2022 [26] & Deep learning can detect and sort different types of solid waste. \\ \hline \hline \end{tabular}
\end{table}
Table 1: The insights from the selected papers.