## 3 Materials and Methods  
This paper proposes a residential automatic waste sorting bin for recycling. It allows users to sort without being aware of the process. The main idea of this system is that the user throws the waste into the bin without thinking about which compartment the object should be placed in. The waste is automatically redirected to the corresponding compartment through the material sorting process. This prototype has four separate compartments corresponding to the four major waste categories: glass, plastic, cardboard, and metal. This concept is based on implementing an automated waste sorting bin for recycling, using image processing algorithms and material recognition to determine the type of waste being thrown away. The system identifies materials from different types of waste, such as glass, plastic, cardboard, and metal, and directs these materials to a specific compartment without user intervention. RBin is equipped with a two-level sorting mechanism ensuring redirection to the correct compartment, depending on the material types. * Sorting level 1. At the top of the system, RBin has a flap that redirects an object to the left or right, depending on its characteristics. This flap is driven by a stepper motor, which ensures the object's movement to the left or right, thus allowing an initial separation. * Sorting level 2. After the object is redirected to one of the two initial paths (left or right), it reaches level 2 of sorting, where additional sorting occurs in the forward and backward directions. At this level, physical separator elements are placed along the object's path, allowing the materials to be sorted into the corresponding containers. * Final collection compartments. After the object has been sorted through the two levels, it reaches the final compartment corresponding to its material. Thus, the front-left compartment is designated for metal waste, the left-back compartment corresponds to plastic waste, the right-front compartment is designated for glass waste, and the right-back compartment collects cardboard. There are separate elements of plexiglass on each level, so the objects cannot roll into the other plane once they reach level 2. RBin uses two NEMA23 stepper motors with TB6600 drivers, 4 A and 9-40 V, to control the redirection flaps of the objects. RBin includes an integrated Elgato Neo Facecam to enable classification that captures images of the object placed on the collection flap. The employed algorithm for classification runs on a Raspberry Pi 5. The algorithm acquires the data as an image in real time, processes and extracts the information about the object, and makes the decisions for waste sorting. The visualization of the sorting process progress is reported to the RBin Raspberry Pi display, which shows information about the categories of collected materials. This screen was added as an additional informational tool for the user. However, in the commercial version of RBin, the visualization screen may be removed to reduce energy consumption and cost. Figure 1 presents the proposed hardware prototype previously described. The total cost of the prototype is about 563 EUR. Raspberry Pi 5 is the central processing unit of the prototype, acquired for 89.9 EUR. It is paired with a Raspberry Pi display, priced at 69.8 EUR. The system is powered by a Pi 5 27 W power supply with USB-C, which costs 13.4 EUR. Two NEMA 23 stepper motors control the sorting mechanisms' movement. The total cost for these motors is 36 EUR. Along with the motors, motor drivers control them, which adds 16 EUR to the cost (Table 1). Figure 1: RBin hardware prototype.