## 3 Experimental Design
### Experimental Results and Discussion  
Initially, the recognition performance of the target detection methods based on OpenCV and YOLOv8 is tested at different positions with the same posture. Within the range of successful target recognition, the coordinate prediction values are calculated and their docking success rates are tested, as shown in Appendix A in Table A1. The error between the predicted point and the actual point is represented by the Euclidean distance. From Figures 8 and 9, it can be observed that when the fuel tank opening moves in a single direction, the prediction error is relatively small. However, when moving in multiple directions, the error increases significantly. The largest error in prediction occurs when the target object moves along the y  
The errors are analyzed from both positional and attitudinal perspectives. As can be seen in Figure 10, when there is a change in attitude, the predicted values of the target center point based on OpenCV are significantly higher than those obtained using the YOLOv8 method. When there is a change in a single attitude, the positional error does not vary significantly. However, when the attitudes of multiple axes change, the error in predicting the center point of circular (elliptical) detection using OpenCV is significantly higher than that of using the midpoint of the target box for equivalent substitution based on YOLOv8. It is also evident from the figure that when the error in attitude prediction is large, the positional error increases correspondingly. This indicates that the accuracy of attitude estimation greatly influences the determination of the target's position. Additionally, the robustness of using OpenCV for origin localization is not strong. The stability of docking operations is poor when there are significant changes in the object's pose. In contrast, the YOLOv8-based pose detection method demonstrates better robustness. Finally, within the range where both methods can recognize the target, ten sets of points are randomly set up to test their recognition success rate and record the time taken for docking by each, as shown in Appendix A in Table 11.  
In Figure 11, the red section represents the time required for unsuccessful docking attempts. As we can observe from the graph, the positioning method based on OpenCV requires more time and has a significantly lower success rate compared to the method based on YOLOv8. Further analysis of the graph reveals that the average time taken for docking operations using the traditional OpenCV-based object detection method is 10.61 s. In contrast, the docking operation using the YOLOv8-based object detection method averages 9.93 s, which is a 6.8% reduction in time required compared to the traditional method. Additionally, the YOLOv8-based method is capable of successfully completing docking operations over a broader range. The experimental results show that the target recognition method based on OpenCV can detect the target refueling port only within a translational range of 10 cm and a rotational range of 3\({}^{\circ}\). It can successfully dock the refueling port only within a translational range of 10 cm and a rotational range of 2\({}^{\circ}\). In contrast, the target recognition method based on YOLOv8 can accurately identify the target connector within a translational range of 10 cm and a rotational range of 5\({}^{\circ}\). Failure in docking the refueling port only occurs when there are significant translations of about 10 cm in the x, y, and z axes, and substantial rotations in all three axes (rx, ry, rz). Compared to the traditional OpenCV-based target detection method, the YOLOv8-based recognition method has a broader recognition range and higher accuracy. The pose estimation derived from the same method is more precise, leading to a higher success rate in target docking.