## 1 Introduction  
Since the beginning of the first industrial revolution, the industry has experienced continuous waves of innovation [1]. In the current era of Industry 4.0, digitization and the application of Information and Communication Technologies (ICT) are driving the development of intelligent manufacturing systems and smart factories, significantly enhancing operational efficiency [2, 3]. A key component of this transformation is the deployment of collaborative robots (also known as cobots), which are designed to facilitate safe and effective human-robot interaction [4]. These robots can be programmed using a variety of methods, including manual guidance, to enable smooth and seamless collaboration. Meanwhile, computer vision technology has empowered machines to process and interpret visual information, revolutionizing fields such as healthcare and automotive manufacturing. The integration of machine vision with collaborative robots further enhances efficiency and adaptability in production environments [5]. While industrial robots have significantly contributed to automation and productivity, they can pose potential risks to operators if safety measures are not adequately implemented. These safety concerns have been partially addressed through the development of cobots. Cobots are designed to enable safe interaction and communication between human operators and other robots within shared work environments where close human-robot proximity is common [6]. The safety features of collaborative robots include the use of lightweight construction materials, rounded edges, and inherent limitations on speed and force, which are enforced through sensors or specialized software to ensure safe operation [7]. However, it is important to note that these inherent limitations in speed and force may restrict their effectiveness in applications that require high-speed or high-capacity processing. Industrial robots can be programmed using several methods, with the most common approaches including offline programming, teach pendant programming, and manual robot guidance [8]. Offline programming involves configuring the robot's workspace and developing its control program on a computer, allowing for detailed planning and simulation before deployment. Teach pendant programming utilizes a handheld device to remotely control and program the robot, providing a user-friendly interface for direct input. Manual robot guidance, on the other hand, involves the programmer physically guiding the robot through the desired motions, during which the robot records the path, tasks, and end positions. This method is particularly well-suited for collaborative robots, where seamless cooperation between the operator and the robot is essential for effective task execution. Alternatively, computer vision represents a cutting-edge area of modern technology, fundamentally transforming human interaction with the world by enabling machines to interpret and comprehend visual information [9; 10]. As an interdisciplinary field, it merges artificial intelligence with advanced image processing techniques to replicate--and in some cases, exceed--human visual perception. Through computer vision, machines are capable of recognizing patterns, identifying objects, and extracting meaningful insights from images and videos, driving innovation across diverse sectors such as healthcare, automotive, entertainment, and security [11; 12]. The integration of machine vision with cobots offers significant opportunities for various industries. By combining these technologies, companies can enhance production efficiency and flexibility, optimizing processes and improving overall operational capabilities [13]. This study focuses on developing a robotic workstation equipped with a collaborative robot capable of supplying tools to a human operator by detecting them through a camera and interpreting operator gestures.