## 3 Review of Papers Dealing with the Application of Robots in Waste Separation
### The Major Findings of the Selected Papers
Recognition is performed by applying algorithms that are trained on a training set to recognize and classify a specific type of waste or element. The success of detecting recycling objects depends on the performance of the vision system, algorithms, and training set. Certain databases are available for training recognition algorithms that also contain e-waste data, but as e-waste is very diverse, often the database of one case cannot be generalized and applied to others [23; 26; 29]. The main components of the vision system are [26; 29]:  
1. Sensors: most commonly cameras and/or optical sensors. 2. Image processing hardware: consists of processors (e.g., microprocessors, a Digital Signal Processor (DSP), and a Field Programmable Gate Array (FPGA)) that enable fast processing of captured images and memory that serves to temporarily store data during image processing. 3. Image processing software, which can be separated into two parts: 1. Image analysis algorithms that include algorithms for filtering, segmentation, pattern recognition, classification, and other visual data processing; 2. Machine learning and artificial intelligence, where algorithms are developed that use deep learning techniques to recognize and interpret complex visual patterns. There are multiple reasons that make the application of computer vision in e-waste recycling highly desirable, such as the following [27]: reduction in overall recycling costs, speeding up the process, easier process control, centralized database, real-time information, quality control, high precision, etc. A robot system with grippers assigns the robot the task of selecting an item for recycling, sorting it, and disassembling it, either independently or with human assistance. The robotic system's performance influences the speed and precision of the separation process, while the gripper selection determines the reliability of grasping the object. The choice of grippers is large and depends on the shape and dimensions of the object being recycled (e.g., two-finger, vacuum, multi-finger, or usage of multiple grippers) [23]. The development of new technologies, like the collaborative robot, holds significant potential for use in recycling and waste separation, leveraging the cooperation between humans and robots, a concept known as human-robot collaboration (HRC). HRC makes it possible to use the strength, precision, and speed of the robot, while on the other hand, humans can contribute with cognitive abilities that the robot cannot fully replicate (e.g., skills, experience, creativity, etc.) [24; 30; 31]. According to [PERSON] et al. [30], it was established that the speed of recycling and the success of catching objects increase depending on the level of human involvement in the waste separation process, and that recycling is globally faster than when only a human or a robot work independently. Human involvement can be divided into three levels, where each level of involvement implies previous levels [30]:  
* Level 1: Occlusion removal--removing objects that overlap other objects makes it easier for the vision system to recognize objects and capture them later. * Level 2: Optimal distance--moving the object to allow enough space for the robot's gripper to grasp the object. * Level 3: Optimum Grasping Position--placing the item to be sorted in a position that is ideal for grasping by the robot.