## 3 Results
### Dynamic Grasping
#### 3.3.1 Prediction Performance of the KF  
Figure 8 illustrates the dynamic grasping process on the production line and the relationship between various coordinate systems. The conveyor belt carries oysters into the vision system, where images are captured. The upper-level computer system performs object recognition on the oysters and predicts their positions. Subsequently, the lower-level computer system controls the robot to execute the grasping motion based on the signals received. The direction of the conveyor belt is along the x-axis of the robot coordinate system, while the x-axis direction of the camera coordinate system is opposite to that of the robot coordinate system. Therefore, the oysters move from right to left in the camera field of view. After entering the field of view, the YOLOv8-OBB algorithm was used for real-time detection, and according to the initial state of oysters, the method combining KF and Low-Pass filters was used to refine the prediction results. The KF combines previous information with the most recent measurements to provide the best estimate of oyster location, while the Low-Pass filter suppresses high-frequency noise to ensure smoother and more stable predictions. This combination effectively balances accuracy and stability, addressing challenges such as noisy measurements and sudden position fluctuations. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Number of Oysters** & **Number of Samples** & **Number of Grabs** & **Accuracy (\%)** \\ \hline
1 & 15 & 15 & 100 \\
2 & 15 & 30 & 100 \\
3 & 15 & 47 & 95.74 \\
4 & 15 & 65 & 92.30 \\ Total & 60 & 157 & 95.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of static grasping. Figure 8: Schematic diagram illustrating the dynamic grasping process of an oyster and the relative positioning of various coordinate systems. In Figure 9, the green dots represent the location of the oyster grab detected by yolovii-obb, and the blue dots represent the predicted location of the oyster grab in the next frame determined by the KF and Low-Pass filter. Initially, the predicted positions lag slightly due to the weight coefficients of the KF not being fully updated and the smoothing effect of the Low-Pass filter. However, after several iterations, the predictions become more accurate and stable, reducing noise-induced errors. To ensure sufficient iterations, a prediction range is established in the center of the image field of view. When the horizontal coordinates of the grasping point reach this range, the best estimated position and the average velocity of the grasping point, computed through the combined filtering process of the current and previous time frames, will be used to predict the position of the grasping point t seconds earlier. Here, t represents the typical time required for the gripper to move from its initial position to the grasping range, as determined through empirical testing. Figure 10 illustrates the effectiveness of the KF combined with two different smoothing techniques (Moving Average and Low-pass filter) across three conveyor belt speed scenarios: (a) high speed, (b) medium speed, and (c) low speed. The blue line represents the raw grasp point predictions obtained directly from the KF, showing significant noise and fluctuations. The orange line corresponds to the predictions refined using the KF combined with Moving Average, showing reduced noise but introducing minor delays in the grasp point predictions. The green line denotes predictions processed with the KF combined with the Low-Pass filter, achieving smoother and more stable results with minimal delay, particularly evident at higher speeds. In terms of system performance, our average latency for image acquisition is 15 ms. The YOLOv8-OBB inference latency averages 42 ms, while the KF prediction latency is 10 ms. At the same time, ROS Rviz is utilized to monitor node statuses in real time and record the Topic transmission frequency.