Abstract
The traditional manual concrete vibration work faces numerous limitations, necessitating efficient automated method to assist in this task. This study proposes a vision-based continuous concrete vibration method for vibrating robots. By enhancing the YOLOv8n model with attention mechanisms, our proposed method demonstrates a high AP of 93.31 % in identifying reinforcing grids, and an FPS of 25.6 on embedded systems. For the first time in concrete vibration tasks, this study utilizes spatial positional information to cluster coordinate data, transforming confidence-sorted data into spatially ordered sequences. Vibrating robot case test shows that the proposed method enhances the vibration speed by 22.18 % and improves the vibration success rate by 11.67 % compared to traditional strategies. Additionally, the on-site experiment conducted at four construction sites demonstrated the robustness of the proposed method. These findings advance automation in concrete vibration work, offering significant implications for the fields of robotics and construction engineering.
Published Version
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