# Small-Scale Zero-Shot Collision Localization for Robots Using RL-CNN
## 5 Experiment Results and Discussion
### Discussion  
As shown in Table 5, there are obvious differences in the accuracy of predicting zero-shot collision points in different cases. Table 6 shows that the F-scores of R4-R7 are considerably lower than for other regions. Since this phenomenon exists with different models, it can be explained by flaws in the data. These flaws may be caused by the following two aspects: one is that there is vibration during the operation of the cable-driven manipulator, and the other is that the end faces of the charger and the charging port are not completely parallel. From the results, in a millimeter-level contact scenario, the deviation between the ideal collision point and the actual collision point caused by these two aspects will greatly affect the localization performance of the data-driven collision localization method. Unfortunately, due to structural characteristics, the vibration amplitude of the cable-driven manipulator is often larger than that of the joint direct-drive manipulator, and this vibration is often difficult to eliminate. In addition, in practical engineering applications, it is very difficult to ensure that the end faces of the charger and the charging port are parallel when collecting collision point data, especially when a large amount of data needs to be collected. Thus, the deviation is often unavoidable. As shown in Table 6, compared to other methods, the proposed method has better localization ability for these samples with high similarity, which is especially obvious in the case of eight-region localization. For example, in R4, the F-score of the proposed method is 95.25% higher than the next highest F-score. This, combined with the results in Table 7, can indicate that the root stage of the proposed method isolates the collision similarity caused by the asymmetric assembly of the compensator and the charger, which helps the method in the leaf stage to focus more on the localization of the finely divided regions. This scheme alleviates the impact of the deviation to a certain extent. In addition, by comparing the localization effects of DCNN and BN-DCNN in different regions of the collision points in Tables 6 and 7, it can be seen that BN is effective in improving the network's ability to locate zero-shot collision points. Although the zero-shot collision points and other collision points in the same region belong to the same class, there are obvious differences on the mm scale. Thus, the localization estimation of the zero-shot collision point requires the model to have good generalization ability  
called RL-CNN. The vibration signals in the elastic compensator (used to connect the charger to the end link of the manipulator) were used to train the proposed model. In order to explore the localization effect, we divided the end surface of the charging port into eight radial regions. In terms of simulating the zero-shot collision point, we selected one collision point from each region as the zero-shot collision point, and we ensured that the information on the zero-shot collision point was not leaked during the training process. The test results of the simulation experiment confirmed that the proposed method has a promising effect on zero-shot collision point localization. The conclusions may be summarized as follows:  
1. The proposed method has been proven to be able to achieve zero-shot collision localization in the millimeter-scale area. The method does not require complex prior expert knowledge, and the collision localization can be achieved using raw data, which is easier to implement in real application scenarios. 2. The introduction of the root stage can effectively reduce the impact of the collision signal similarity caused by the non-central installation, and it helps the leaf stage to focus more on the finely divided subregions in each isolated region. This further enables the model to resist the loss in accuracy caused by the deviation between the collected collision point and the ideal collision point. 3.