# Enhancing Safety in Automatic Electric Vehicle Charging: A Novel Collision Classification Approach
## 3 Materials and Methods  
### Dataset Construction  
To validate the effectiveness of the proposed method, we designed an experimental setup, as illustrated in Figure 1. The experimental setup primarily consisted of a charging robot based on an Aubo-i5 and a test platform. The charger was connected to the charging robot through a flexible wrist, and an Inertial Measurement Unit (IMU) was mounted above the charger to capture vibration data during collisions with the charging port. The vibration signal comprised three-axis acceleration and three-axis angular velocity. The test platform was capable of vertical movement and yaw adjustment, providing different poses for the charging port in space. To obtain reasonable data related to collisions between the charger and the charging port, it was necessary to describe the data acquisition method. In line with the actual application scenario, the robot connected the charger and the charging port in a linear motion, as shown in Figure 2. The linear motion speed at the end effector of the robot was set to 15 mm/s, and the IMU had a sampling frequency of 1500 Hz. Regarding the relative position during a collision between the charger and the charging port, we adopted the approach described in [15]. Specifically, we defined the intersection of the center axis of the charger and the plane where the end face of the charging port was located as the collision point. Based on this description, we designed a collision point template, consisting of 289 points in total, arranged in a grid of 17 rows and 17 columns, with adjacent points spaced 1 mm apart. This collision point template effectively captured situations where there was incomplete alignment and deviation between the charger and the charging port during the plug-in process. In the plug-in process, due to the presence of a flexible wrist, when the deviation was within a certain range, even if there was a collision between the charger and the charging port, the charger could still be inserted into the charging port. We defined the collision points in such situations as acceptable collision points. When the deviation exceeded a certain range and the collision prevented the charger from being inserted, or if insertion would have caused significant deformation in the flexible wrist, leading to potential plastic deformation, we defined the collision points as vulnerable collision points. Figure 1: Illustration of experimental setup and collision domain. In this study, impedance control strategies were not considered during the experiments. We solely focused on the scenario where the robot connected the charger to the charging port in a linear motion. Theoretically, the introduction of impedance control could enlarge the acceptable domain, but this aspect is beyond the scope of this paper's discussion. It is worth noting that due to the non-coaxial alignment between the charger and the flexible wrist, when the charger came into contact with the charging port, it induced a counterclockwise rotation along the \(y\)-axis. Therefore, the acceptable domain in the experiment exhibited asymmetry relative to the horizontal centerline of the charging port. Specifically, it was composed of 15 acceptable collision points and 1 center collision point from the collision point template. The region formed by the remaining collision points in the template was defined as the vulnerable domain. The primary objective of this study was to predict whether collisions occurred in the acceptable domain or the vulnerable domain. As mentioned in [14; 15; 30], theoretically, variations in joint configurations at the moment of impact may impact the collision processing methods based on vibration signals. In the data collection process in this study, data were also collected at different joint configurations at the moment of collision.