Advancing Automation With Cloud-Connected Robotics

Abstract

This thesis focused on the development and implementation of an automated, cloud-connected robotic system designed to enhance precision, efficiency, and reliability in medical device fine-tuning processes. The system integrated a robotic arm (Ned2), a load calculator, and a sensor-reading device, with AWS cloud services facilitating seamless communication and data exchange. The project aimed to minimize human intervention, reduce error rates, and achieve consistent fine-tuning outcomes through advanced automation. Key methodologies included the design of a robust system architecture, programming in Python, C, and C# for device control and data processing, and the incorporation of freeRTOS for real-time task management. Custom components were designed and fabricated using 3D printing technology to meet specific system requirements. The MQTT protocol was employed for efficient messaging between devices, ensuring precise coordination dur ing fine-tuning operations. The research involved detailed stability analysis and iterative adjustments to achieve optimal performance. Data visualization and validation methods were used to assess the effectiveness of the system. The results demon strated significant improvements in process accuracy, repeatability, and operational efficiency compared to tra ditional manual methods. This work contributes to the field of healthcare automation by offering a scalable, cloud-connected solution for device fine-tuning. It lays a foundation for further research and innovation in integrating robotics with healthcare technologies to meet growing demands for precision and efficiency in medical device manufacturing and testing.