Vision Guided Sorting of Medical Dissection Tools using a 3DOF SCARA Robot with Deep Learning Integration

Integrating artificial intelligence with robotic systems has paved the way for innovative automation solutions across diverse domains. This study proposes sorting medical dissection tools using an innovative integrated system comprising computer vision and robots. The system employs a single conveyor line with a modified 3-degree-of-freedom (3DOF) SCARA robot to pick and place the dissection tools from the conveyor to the respective bins. Computer vision and deep learning algorithms, precisely the YOLOv5 model, identify and categorize dissection tools in real-time. Deploying a 3DOF robotic arm enhances efficiency by sorting tools and placing them in designated locations based on their classes. Unlike conventional methods that use multiple conveyors, this approach significantly reduces infrastructure costs while simplifying the sorting process. This automated sorting method not only minimizes errors associated with manual sorting but also offers a cost-effective solution for manufacturing firms. Its adaptability makes it suitable for sorting a diverse range of dissection tools, contributing to increased operational efficiency and cost savings in manufacturing. This integrated, automated, vision-based system ensures efficient sorting with minimal error rates, making the system suitable for laboratory and surgical settings where accuracy and efficiency are paramount.