## 3 Materials and Methods  
The core of the work focuses on controlling a robot based on visual inputs from the operator, which led us to select cameras as the primary devices for capturing gestures. In the realm of non-contact gesture sensing, cameras offer an excellent solution for accurately and dynamically capturing the operator's movements, eliminating the need for physical contact with sensors and allowing unrestricted movement within the workspace. Additionally, cameras are employed to detect objects on the work surface. In the analysis of the current state of the problem (Section 2), it was identified that RGBD cameras are most frequently used for both gesture and object detection. These cameras, which combine color and depth imaging, provide comprehensive information that is particularly beneficial for tasks involving object recognition and localization in three-dimensional space. The objective of the design is to simplify the work environment and enhance the efficiency of managing the cobot. However, in the current approach, depth information acquired by the RGBD camera was not utilized for tool segmentation. The system exclusively relied on visual data from the RGB camera for detecting and recognizing tools. While depth data could potentially enhance segmentation accuracy, particularly in more complex or occluded scenarios, it was not incorporated into the present framework. Future work might explore the integration of depth information to improve segmentation and object recognition, particularly in environments where depth cues can aid in distinguishing objects in cluttered or dynamic scenes. In the workplace, static gestures are employed to control the robot. The set of static gestures includes commands for initiating and halting processes (start/stop), confirming selections (OK), and representing quantities or numbers (ranging from 1 to 7). Compared to dynamic gestures or complex hand movements, static gestures are generally simpler and easier to learn, making them more accessible to a broader range of users, including those without extensive training in robotics or technology [47, 48]. Moreover, static gestures offer clear and precise instructions, minimizing the risk of misinterpretation. While the current approach focuses on static gestures, dynamic movements, such as continuous tool-handing trajectories, could provide additional contextual information and significantly enhance the model's ability to interpret and predict human intentions more accurately. In real-world robotic applications, tool exchanges and other interactions often involve smooth, sequential motions rather than isolated static postures. Capturing these temporal dependencies could enable the system to better recognize patterns in human movement, anticipate actions, and facilitate more natural and responsive robotic assistance. Acknowledging these potential benefits, future work might explore methods to incorporate temporal information, enabling the model to generalize beyond static postures and improve its adaptability in complex, real-world environments where seamless human-robot collaboration is required. As determining the distance between the hands and the camera is unnecessary for gesture recognition, a standard webcam will suffice for capturing hand gestures. To extract these gestures from the webcam feed, computer vision algorithms are required. The algorithm's role will be to detect gestures from both hands, and for this purpose, a single-step YOLO detection algorithm has been chosen. Object detection involves determining the precise position and orientation of objects relative to the robot, enabling the collaborative robot to manipulate these detected objects effectively. Implementing object detection in a workspace with a collaborative robot presents several challenges, including selecting the objects the robot will handle and positioning the camera correctly above the workspace to ensure comprehensive coverage of all objects.