# Low-Cost Data-Driven Robot Collision Localization Using a Sparse Modular Point Matrix
## 4 Conclusions  
To achieve higher precision in collision localization, the existing data-driven method for the plug-in process of electric vehicle (EV) automatic charging suffers from high data collection costs. In this study, we propose a novel data-driven approach for robot collision localization specifically tailored to automatic charging scenarios for EVs, effectively mitigating this issue. Our method is grounded in a collision point matrix template and integrates a sparse modular point matrix (SMPM) to reduce the necessary size of the collision dataset for data-driven techniques. By employing an optimized SMPM structure to sparsify the entire template, we achieve a reduction in data scale of 48.15% while maintaining an average localization accuracy of 89.43% and an average RMSE of 1.64 mm. Compared to scenarios without sparsification, the average localization accuracy decreases by only 8.72%, with a minimal increase of 0.37 mm in RMSE for collision localization. Additionally, we exploit the characteristics of convolutional neural network (CNNs) and echo state network (ESNs) to develop an integrated adaptive extractor for dynamic feature extraction from collision vibration signals. Utilizing a support vector machine (SVM) as the classifier, we demonstrate the exceptional performance of the model in addressing collision localization issues when combined with the SMPM. Specifically, even with a 48.15% reduction in data scale, our model achieves an outstanding collision localization accuracy of 91.27% and an RMSE of 1.46 mm for collision localization. Although our proposed method effectively reduces dataset size while maintaining collision localization performance at a high level, there is still a noticeable decrease in collision localization accuracy when compared to the scenario without any sparsification. In subsequent research, we will focus on exploring whether data augmentation techniques can be employed to generate data for sparsified points, creating a virtual supplement to the dataset. We aim to enhance the performance of the collision localization method further while reducing the need for experimental data acquisition. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Case** & **CE-SVM** & **CNN-SVM** & **LSTM-SVM** & **ENS-SVM** & **GRU-SVM** \\ \hline S1 & **1.46** & 1.62 & 1.73 & 1.63 & 1.75 \\ S2 & **1.4** & 1.52 & 1.59 & 1.54 & 1.61 \\ S3 & **1.37** & 1.46 & 1.51 & 1.5 & 1.49 \\ S4 & **1.33** & 1.45 & 1.48 & 1.47 & 1.48 \\ S5 & **1.34** & 1.39 & 1.54 & 1.42 & 1.42 \\ S6 & **1.3** & 1.39 & 1.45 & 1.42 & 1.44 \\ S7 & 1.25 & **1.24** & 1.35 & 1.26 & 1.26 \\ \hline \end{tabular}
\end{table} 
Table 4: Collision localization RMSEs achieved using SMPMs with different levels of sparsification (mm). \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Case** & **CE-SVM** & **CNN-SVM** & **LSTM-SVM** & **ENS-SVM** & **GRU-SVM** & **Data Scale** \\ \hline S1 & **91.27\%** & 89.29\% & 88.33\% & 89.53\% & 88.73\% & 51.85\% \\ S2 & **94.29\%** & 91.79\% & 90.62\% & 91.54\% & 90.8\% & 59.26\% \\ S3 & **95\%** & 94.17\% & 93.24\% & 93.27\% & 93.06\% & 66.67\% \\ S4 & **96.2\%** & 95\% & 93.92\% & 94.2\% & 93.88\% & 74.07\% \\ S5 & **97.07\%** & 95.12\% & 94.44\% & 95.22\% & 94.72\% & 81.48\% \\ S6 & **96.73\%** & 95.74\% & 94.78\% & 95.65\% & 95.28\% & 88.89\% \\ S7 & 98.49\% & **98.67\%** & 96.7\% & 98.64\% & 98.27\% & 100\% \\ \hline \end{tabular}
\end{table} 
Table 3: Collision localization accuracy achieved using SMPMs with different levels of sparsification. **Author Contributions:** [PERSON] developed the methodology; [PERSON] conceived and designed the experiment; [PERSON] and [PERSON] conducted the data curation and collection; [PERSON] wrote the original draft of the paper; [PERSON] and [PERSON] reviewed and edited the paper. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.