# Small-Scale Zero-Shot Collision Localization for Robots Using RL-CNN
## 2 Related Work  
As demonstrated in [20], the collision localization problem is essentially a classification problem. Unlike the collision classification task, which cares whether the collision is accidental or intentional, collision localization can be considered as a multi-classification problem with data acquisition boundary condition constraints. Collision localization often provides information for the subsequent collision response or assists in the completion of collision classification to improve the reliability of collision classification. Since the process of collision has temporal characteristics, this kind of classification problem can often be converted into a classification problem of time series signals. Recently, related work has mainly used two types of signal-processing methods. One type consists of machine learning methods relying on manual feature extraction. In [20], the joint torque signal was collected in a specific motion mode, a variety of machine learning classifiers were used to filter out artificial features, and, finally, online collision classification was realized using NN and Bayesian decision theory. In [21], an artificial neural network was used to analyze the time domain and frequency domain characteristics of the vibration signal caused by the collision and then determine the collision localization from preset positions on different arms. Although this kind of method using artificial features is cheap in terms of classifier training and actual engineering application deployment, the manual extraction of features often relies on expert knowledge, and, when using such features, it is often impossible to update the corresponding features according to the classification results. When encountering complex problems, the effectiveness of such a method will decrease. In our previous work, we confirmed that using this kind of feature engineering method to deal with small-scale collision problems is not ideal. The other kind of method used in the literature is the automatic feature extraction method, which is capable of using raw data directly without prior feature engineering. The representative types are the RNN-based method and the CNN-based method. For example, in [14], the RNN-based method was used to solve the classification problem of distinguishing between intentional and accidental collisions between humans and robots, and it achieved good results. However, there are very few studies using RNN-based methods to explore the problem of robot collision localization. Theoretically, the RNN-based method has natural advantages for time-series signal processing; LSTM-based and GRU-based methods, especially, are widely used in EEG analysis [22], music emotion classification [23; 24], and body pose estimation [25; 26; 27]. In our previous work, it was confirmed that the effect of using a two-layer CNN is slightly better than using LSTM when locating the pre-specified collision points. Therefore, CNN is used as the basis of this work. Despite recent progress in collision localization using both artificial feature extraction and automatic feature extraction methods, there are still limitations in the existing studies. Most existing methods focus on large-scale collision localization problems, such as determining which link of the robot arm a collision occurred on. However, it is unclear whether these methods can be applied to small-scale collision problems, and the effectiveness of the signal used and the structure of the device must be considered. In some cases, it may be necessary to add external structures or sensors to the robot, and the suitability of the scenario used must also be considered. In our previous work, we proposed a data-driven method using external compensator vibration signals for studying small-scale collision localization problems in the context of electric vehicle automatic charging scenarios and achieved some success.