## 5 Discussion  
This work successfully integrated tool detection, gesture recognition, and robotic control to create an efficient robotic workplace for feeding tools. The development began with generating a synthetic dataset of 150,000 images featuring various hand tools, which was crucial for training a robust computer vision algorithm. Initial challenges included adapting models such as YOLACT and YOLOv8 to detect tools accurately under diverse conditions. The switch to YOLOv8 proved effective in addressing issues with tool segmentation and rotation detection. Gesture recognition was seamlessly incorporated to allow the operator to control the robotic assistant intuitively. The final phase involved setting up the ABB Yumi robot in a workspace where the system could dynamically respond to operator inputs and handle tools with precision. Despite overcoming significant challenges, such as dataset creation and model training, the integrated system demonstrates significant potential for improving productivity and ergonomics in tool handling, showcasing the effectiveness of combining synthetic data, advanced vision models, and collaborative robotics in a real-world application. The result of the first phase was multiple synthetic image datasets, a trained computer vision algorithm for tool detection, and a Python program integrated within ROS 2. The software solution consisted of approximately three nodes that communicated based on topics and a publisher/subscriber model. This program facilitated image segmentation and tool detection from RGBD images captured by the Intel RealSense camera. From the detection and segmentation results, the removal position for the robot was determined by locating the center of the bounding box of the selected tool, while tool rotation on the table was assessed using the tool's mask. The custom-trained YOLOv8 model was employed for detection and segmentation, providing a comprehensive solution for tool handling in the robotic workspace. The designed robotic workplace and control method reveal several limitations that need to be addressed for improved functionality and effectiveness  
Figure 15: Robotic workplace GUI. * The ABB Yumi robot, with a maximum payload of 0.5 kg, imposes constraints on the type of tools it can handle. This limitation restricted the choice of tools used in the system to lighter models and impacted the use of real tools, as heavier or bulkier tools could not be accommodated by the robot's grippers. * The robotic workspace currently lacks a variety of gripping effectors, which limits the robot's ability to handle different tool types and sizes effectively. The absence of adaptable or interchangeable end effectors could restrict the robot's functionality in diverse operational scenarios. * Due to the robot's limited payload capacity, the system utilized synthetic models of tools rather than actual tools. This substitution may not fully capture the complexities and challenges associated with handling real-world tools, potentially affecting the accuracy and reliability of the tool detection and manipulation process. * The current gesture-based control system requires the use of both hands to denote tool numbers 5 through 7. This requirement could complicate the gesture recognition process and may not be as intuitive or user-friendly as single-hand gestures, which could impact the efficiency and ease of use for operators. * The method of mounting cameras in the current setup showed limited robustness and flexibility. The camera positioning needs to be improved to ensure stable and consistent image capture, which is crucial for accurate tool detection and gesture recognition. * The articulated movement of the robot and the method for tool delivery to the operator require further optimization. Enhancements are needed to ensure smooth and precise tool handover, which is essential for effective operation in a real-world setting. The use of a dual-armed collaborative robot introduced significant constraints in the implementation of the robotic workplace.