Integrating Tiny Machine Learning and Edge Computing for Real-Time Object Recognition in Industrial Robotic Arms

By integrating visual recognition technology and multi-object recognition into robotic arms, the flexibility and automation of the production process were improved in this study. By applying tiny machine learning (TinyML) and machine vision algorithms, we integrated edge computing devices to control the robotic arms and identified objects precisely on the production line, with ultra-low energy consumption. The developed system in this study included the SparkFun Edge development board and Raspberry Pi Camera Module 3, as edge devices for data processing, image recognition, and robotic arm control. By utilizing the Edge Impulse platform for data collection, model training, and optimization, edge devices and models for use in resource-limited environments were successfully generated. Using Edge Impulse’s automated toolchain, real-time image processing and object recognition were realized. The system achieved improved recognition accuracy and operational speed, demonstrating the potential of TinyML in enhancing the intelligence of robotic arms. MariaDB was chosen for data storage. Grafana was used to design a user-friendly web interface for real-time data monitoring and visualization and immediate data analysis and system monitoring. The developed system presented a success rate of 99% during actual operation. The feasibility of combining advanced image processing technology with robotic arms in intelligent manufacturing was verified in this study. The potential of integrating machine learning and automation technologies was also confirmed for the development of future manufacturing technologies. The model provides a technical reference and ideas for future factories that require high levels of automation and intelligence.