## 5 Results and Discussion of Physical Experiment
### Object Detection and Positioning  
Based on the PyTorch framework, the operating environment is set up, the dataset is trained using the YOLOv5 model, the number of training iterations of the dataset is set to 300 times, and the main data change as the number of iterations increases during the training process. The value Box in Figure 13 represents the average of GIoU loss as a loss function for the bounding box, and the more accurate the value is, the more accurate the target box annotation. Objectness detects the mean loss for the target, and the closer the value is to 0, the more accurate the target detection. Precision represents the ratio of an accurate positive class to all positive classes, and the closer to 1, the higher the accuracy. \[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{10}\]  
Recall indicates the ratio of the correct number of targets marked to the total number of targets, and the closer to 1, the higher the accuracy. \[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{11}\]  
The experimental training effect observes the fluctuation of accuracy and recall, and through the observation of experimental results it can be seen that the precision and recall fluctuation is small. After the training iteration reaches 300 times, the numerical changes tend to be stable, the trained model is assessed, and the recognition effect is as follows. The door-handle recognition and detection models extract large amounts of feature information from images. As shown in Figure 14, the identification results show the frame  
Figure 13: Visualize of results. with a better recognition frame selects the border of the door handle. The recognition rate is as high as 90%, and the experimental results show that the door-handle detection model based on the YOLOv5 model has the characteristics of high precision, good robustness, and fast speed. In this experiment, the vertical distance between the camera and the door plane and the ratio of the length of the recognition frame and the length of the picture is measured in the state of parallel plane constraint. The relationship between the two is described by fitting the curve. Moreover, the vertical distance between the moving robotic arm platform and the door plane is calculated according to the formula of the length proportion substituted into the fitted curve. The experimental design of the door handle and the calculation of the platform is the distance of the mobile robotic arm. The ratio of the length of the recognition frame L to the length of picture x is the vertical coordinate K, and the vertical distance between the camera and the door plane is the abscissa D, with the units in centimeters. The experiment is limited to a distance of 10-100 cm, a total of 20 groups are separated by 5 cm, and each group measures the average of the length ratio K 10 times; the blue discrete data points are sampled, and the scatter points are connected using blue curves. As shown in Figure 15, the red line represents the fitting curve used to approximately depict the functional relationship between the discrete point and the coordinates, which maximizes the approximation between the experimental data and the equation curve. The analysis shows that the length ratio approaches infinity when the spacing approaches zero, the length ratio is zero when the spacing approaches infinity, and the curve is a power series relationship. In addition, the relationship between the length ratio K and the spacing D is a power function obtained through curve fitting. \[\text{K}=11.143\text{D}^{-1.051} \tag{12}\]  
Figure 14: The effect of two types of door handle recognition. The fitted curve in Figure 15 is in line with the actual position of the motion trajectory. According to the fitted curve equation, the data are calculated mathematically, and the error between the calculation spacing and the actual distance is less than 4 mm when being substituted into any length ratio. The fitted curve can better complete the spacing prediction.