Abstract
In the production process, the workpiece must be mounted and fixed on the machine tool before the machine tool performs various processes on the part’s surface. After the process, the workpiece is removed and placed in the corresponding area. In this paper, a robotic, automatic loading and unloading system for small workpieces is designed for machine tools’ automatic loading and unloading. In order to find workpieces in the system, a YOLO-based workpiece object detection algorithm is used to obtain information about the type and location of workpieces in the automatic loading and unloading process. For the problems arising from the experiments, the \(\alpha \)-IOU loss function is used to replace the GIOU loss function in the original network. The experimental results show that \(\alpha \)-IOU significantly improves object detection accuracy compared to GIOU.
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Acknowledgment
The authors would like to gratefully acknowledge the reviewers comments. This work is supported by National Natural Science Foundation of China (Grant No. 52075180), and the Fundamental Research Funds for the Central Universities.
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Wang, N., Lin, J., Zhang, X. (2023). Automatic Loading and Unloading System with Workpiece Identification Based on YOLOv5. In: Yang, H., et al. Intelligent Robotics and Applications. ICIRA 2023. Lecture Notes in Computer Science(), vol 14267. Springer, Singapore. https://doi.org/10.1007/978-981-99-6483-3_51
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DOI: https://doi.org/10.1007/978-981-99-6483-3_51
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