# Enhancing Safety in Automatic Electric Vehicle Charging: A Novel Collision Classification Approach
## 2 Related Work  
Generally, vibration-signal-based collision processing for robots, acknowledged as an engineering-oriented challenge, is essentially the task of classifying time-series signals that exhibit distinctions arising from collisions. Commonly employed methods for processing time-series signals include support vector machine (SVM), k-nearest neighbor (KNN), and artificial neural network (ANN). [PERSON] et al. [14] proposed a collision detection and identification approach for robots, utilizing frequency domain features derived from vibration information coupled with an ANN. This method not only effectively detects collisions but also identifies the specific link where a collision occurred. [PERSON] et al. [21] achieved high-precision collision localization on a single link of a robot by leveraging an SVM in conjunction with a rational strategy for collecting collision vibration information. In addition to utilizing vibration signals to construct collision processing models, there are also methods that leverage joint torque information to address collision-related issues. [PERSON] et al. [8] utilized joint torque signals in conjunction with KNN, SVM, and feed-forward neural network (FNN) methods to classify collisions occurring between robots and humans. This approach effectively distinguishes between intentional contact and accidental collisions involving a specific link of a robot. Additionally, some supervised learning methods based on manual feature extraction in tasks such as robot fault detection and fault-tolerant control, as mentioned in [22; 23], are also worth considering for reference.  
With the advancement of deep learning technologies, the convolutional neural network (CNN) [24] and recurrent neural network (RNN) [25] have been widely applied for the classification of time-series signals. [PERSON] et al. [26] designed a hybrid model combining a CNN and long short-term memory (LSTM) for human emotion analysis based on electroencephalogram (EEG) data. This model successfully classifies human emotions into neutral, positive, and negative categories. [PERSON] et al. [27] integrated an RNN and a CNN to develop a speech recognition method to assist individuals with hearing impairments in translating spoken language into sign language. [PERSON] et al. [28] constructed an efficient bearing fault diagnosis architecture by employing LSTM with a large-margin nearest neighbor algorithm. [PERSON] et al. [29] successfully implemented the diagnosis of non-uniform bearing vibration signals in the presence of disturbances by introducing attention mechanisms into LSTM. In our previous research, we attempted to explore the possibility of simultaneous collision localization and collision classification at the end effector of a manipulator by fusing a CNN with an SVM [15]. However, in practical applications, complex deep learning models often demand significant computational resources, requiring high-end GPUs or dedicated hardware for efficient training and deployment. This significantly increases equipment costs.  
This study makes a significant contribution by proposing a refined collision classification method based on the light gradient boosting machine (LightGBM) that exhibits reduced dependence on GPUs. In order to enhance the robustness of the proposed method against noise interference, we introduce a noise suppression technique based on empirical mode decomposition (EMD) as a preprocessing step for LightGBM training. This strategic approach to collision classification aims to maintain model performance while lowering computational costs, offering a more economically efficient solution for deploying collision classification systems in resource-constrained environments. Simultaneously, this work fills the gap in the related research on collision processing for the automatic charging of electric vehicles in noisy scenarios.