## 4 E-Waste Management in Serbia--Current Situation
### Technical Requirements Analysis and Proposed Solution  
A typical robotic waste separation setup that can be considered in this case, depicted in Figure 5, comprises a pick-and-place mechanism functioning on a conveyor. Guided by a vision system equipped with one or more cameras and specialized algorithms, the robot identifies image content, locates items, and carries out picking tasks. The effectiveness of waste separation systems heavily relies on the computer vision algorithms' ability to recognize diverse waste types and the robustness of grippers handling a wide range of objects [14; 61; 62]. For the purposes of this paper, various approaches may be considered, including color, shape, and volume analysis of detected samples, regarding the possible grippers that may be considered. Figure 4: Manual sorting of ferro-neutral metals (**a**) and recycled samples (**b**). Figure 5: A typical robotic waste separation setup. The technical requirements imposed by the company for the robotized solution are given implicitly through requirements of achieving 95% aluminum purity after sorting and increasing the amount of extracted non-aluminum non-ferrous metals (copper, brass, and zinc) to at least 10%. To meet the requirements, it is necessary to identify crucial aspects which pose challenges towards achieving these goals. The analysis of the current situation considers recycling steps starting just after the Eddy current separator, since all previous steps are overall optimally set to account for the different types of devices which are recycled, and therefore are not subject to alterations. As mentioned earlier, the conveyor output is not consistent in volume due to the discrete nature of the QZ device input. Consequently, the input and output of the Eddy current separator, which follows the QZ processing, are not consistent as well. As a result, the Eddy current separator output conveyor's uneven distribution of material in a single layer presents challenges for both machine vision and object picking operations. One possible solution to a given situation is the introduction of a set of rotary brushes moving in the opposite direction from the conveyor, as well as a vibrating section of the conveyor to ensure an even single-layer distribution. A camera vision system could be used to detect non-aluminum parts on the conveyor and guide the robot to pick them. Initial tests performed on the test sample from the recycling center using an AI algorithm indicate very good classification results with 97.6% objects correctly classified non-aluminum parts. However, the facility in which the sorting is performed features a lot of natural light in addition to the artificial lighting, which may lead to incorrect part position identification due to inconsistent shadow and shine occurrence. To mitigate the sensitivity of the proposed vision system to light conditions and increase its robustness to external/ambient light conditions, it is recommended to shield the detection area from ambient lighting and use only artificial lighting. To understand the challenges related to robotized picking, the following analysis of the hardware requirements relies on the statistical data gathered from the process of recycling refrigerators. In the particular analyzed case, the QZ device outputs up to 20 t per 8 h shift, and up to 5% of the output is a mix of non-ferrous metals. Within the mix, at least 85% is aluminum, which leaves up to 15% of material that needs to be extracted from the conveyor. The observed sample had an average weight of 5,14 g, accounting for 98% of the total mass of pieces that required extraction, with the largest overall sizes ranging from 5 mm to 50 mm [60]. According to the sample's statistics, there are typically 1.013 pieces to pick every second or one piece every 0.9869 s. The layout of the conveyor and depositing trays is such that the pieces' depositing positions are less than 300 mm away from the furthest picking position.