A Fine-Grained Attention Model for High Accuracy Operational Robot Guidance

Deep learning enhanced Internet of Things (IoT) is advancing the transformation toward smart manufacturing. Intelligent robot guidance is one of the most potential deep learning + IoT applications in the manufacturing industry. However, low costs, efficient computing, and extremely high localization accuracy are mandatory requirements for vision robot guidance, particularly in operational factories. Therefore, in this work, a low-cost edge computing-based IoT system is developed based on an innovative fine-grained attention model (FGAM). FGAM integrates a deep-learning-based attention model to detect the region of interest (ROI) and an optimized conventional computer vision model to perform fine-grained localization concentrating on the ROI. Trained with only 100 images collected from real production line, the proposed FGAM has shown superior performance over multiple benchmark models when validated using operational data. Eventually, the FGAM-based edge computing system has been deployed on a welding robot in a real-world factory for mass production. After the assembly of about 6000 products, the deployed system has achieved averaged overall process and transmission time down to 200 ms and overall localization accuracy up to 99.998%.

Automated summary

In this study, a low-cost edge computing-based IoT system is developed, which uses an innovative fine-grained attention model (FGAM) to enhance the accuracy of robot guidance and localization in manufacturing. The FGAM-based system shows superior performance compared to benchmark models and has been successfully deployed in a real-world factory for mass production. The deployed system achieves an average process and transmission time of 200 ms and an overall localization accuracy of up to 99.998%.