# Small-Scale Zero-Shot Collision Localization for Robots Using RL-CNN
## 1 Introduction  
With the increasing popularity of electric vehicles (EVs), the charging of EVs has also attracted much attention. At present, the common charging method in public places involves a human taking the charger off the charging pile and then inserting the charger into the charging port of the EV. However, a charger with a heavy power cable usually brings great inconvenience to the users. In addition, unlike in traditional gas stations where each employee has undergone long-term training and the possibility of mis-operation during the refueling process is very small, in terms of charging electric vehicles, there is a potential safety hazard for the users who have not undergone strict training on how to use the charger by themselves. Therefore, in order to reduce the burden on users and eliminate potential safety hazards, the use of robots to automatically charge EVs has been proposed as an alternative solution [1; 2; 3]. Recently, research on the scenario of automatic charging for EVs has mainly focused on robot control, trajectory planning, and visual positioning of charging ports [4; 5; 6], while less attention has been paid to safety issues, even though safety is crucial during the automatic charging process. In general, in robot application scenarios, accidental contact or collision with the robot is the main safety concern. In order to deal with such problems, some research has explored utilizing a vision system to avoid accidental collisions [7; 8; 9]. However, such methods will fail in the blind spots of the system, and this situation is usually unavoidable in practice. Therefore, in order to improve the safety of robots, research on contact perception is necessary. The contact or collision problem of robots is a very open problem for different application scenarios. These scenarios can be roughly divided into two categories based on whether humans and robots share the workspace or not. In the scenario where humans and robots share the workspace, human safety should be the focus. In this scenario, the threat of the robot to humans often comes from the link of the robot arm rather than the end effector. Considering this situation, it is possible to use model-based methods to analyze and detect the contact [10], determine whether the contact is intentional or accidental [11], and identify the location of the collision [12]. In addition, these works can provide a basis for planning the reasonable response of the robot after contact [13]. Due to the need to artificially set thresholds for the signals used in practice, the noise in different sensors and the complexity of the contact will influence the flexibility and robustness of the method. In the same scenario, the data-driven method is an alternative. For instance, in [14], an RNN-based model is used to realize collision classification. Similarly, in [15], a combination of the Generalized Momentum Observer and the NN method is used to realize the collision classification while also judging whether the collision occurred on the upper or lower part of the robot. However, because the motion of humans has a high degree of randomness, and different motion states of the robot will significantly affect the contact state, it is very difficult to obtain comprehensive contact data. This difficulty in obtaining comprehensive data can be referred to as mode difficulty in data acquisition. To alleviate the mode difficulty in this scenario, it is often necessary to assume that when the robot is in contact with the human, the human is quasi-static and the contact posture with the robot is fixed, and then the contact between the robot and the human is analyzed. However, these assumptions tend to weaken the practical effect of the method. In the other scenarios, where no shared workspace is required, in order to ensure that the robot is able to perform the manipulation tasks reasonably, the contact perception of the robot to the object is also needed.