## 5 Conclusions  
This paper investigates automatic recharging technology for unmanned vehicles based on multi-sensor fusion and proposes a method that integrates LiDAR, depth cameras, and infrared sensors for autonomous navigation and automatic recharging. LiDAR enables accurate environmental perception and obstacle avoidance through remote scanning. The combination of the improved A* algorithm and the dynamic window approach allows the robot to effectively avoid random obstacles while ensuring global path optimality. Infrared sensors are employed for precise docking in the final stage, ensuring accurate alignment with the charging station under various lighting conditions. However, it is important to acknowledge that sensors may be sensitive to different environmental conditions, such as lighting variations and surface reflectivity. This sensitivity can impact the practicality of the experimental results. Future studies should focus on conducting experiments in a variety of real-world conditions to evaluate and improve the robustness of the system. Additionally, assessing the impact of different environmental factors on sensor performance will be crucial. Furthermore, the cost and complexity of the proposed sensors must be carefully evaluated against their benefits in enhancing navigation. Future work should explore more cost-effective alternatives or strategies for sensor integration that maintain performance while reducing overall system complexity. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Test Variable** & **Infrared Only** & **LiDAR Only** & **Vision Only** & **LiDAR + Vision** & \begin{tabular}{l} **LiDAR + Vision** \\ **+ Infrared** \\ \end{tabular} \\ \hline \begin{tabular}{l} Docking success rate (\%) \\ \end{tabular} & 50 & 70 & 50 & 80 & 95 \\ \begin{tabular}{l} Average docking time (s) \\ \end{tabular} & 60 & 45 & 55 & 35 & 25 \\ \begin{tabular}{l} Docking error (cm) \\ \end{tabular} & 15 & 10 & N/A & 8 & 3 \\ \begin{tabular}{l} Obstacle adaptability \\ \end{tabular} & Poor & Medium & Poor & Excellent & Excellent \\ \begin{tabular}{l} Lighting condition adaptability \\ \end{tabular} & Excellent & Excellent & Poor & Good & Excellent \\ \
\begin{tabular}{l} Path planning distance (m) \\ \end{tabular} & N/A & 20 & N/A & 20 & 19 \\
\begin{tabular}{l} Time to reach destination (s) \\ \end{tabular} & N/A & 45 & N/A & 40 & 35 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results comparing different sensor configurations. Moreover, the multi-sensor fusion approach requires real-time processing, raising questions about computational demand and latency. Addressing these challenges will be essential for ensuring that the system operates efficiently and effectively in dynamic environments. Future research should investigate optimization techniques to enhance processing speed and reduce latency in sensor data fusion. In conclusion, the fusion of multiple sensors in the automatic recharging system fully leverages the advantages of each sensor, significantly enhancing the stability and adaptability of the robotic system. The experimental results demonstrate that the proposed method can achieve efficient autonomous navigation and automatic recharging in complex indoor environments, greatly improving system efficiency and reliability. By implementing these suggested improvements, future research can further refine the system's practicality and performance, making it more suitable for real-world applications. Conceptualization, Y.X. and L.W. ; methodology, Y.X. ; software, L.L. ; validation, Y.X., L.W. and L.L. ; formal analysis, Y.X. ; investigation, Y.X. ; resources, L.L. ; data curation, L.W. ; writing--original draft preparation, Y.X. ; writing--review and editing, L.L. ; visualization, Y.X. ; supervision, L.W. ; project administration, Y.X. ; funding acquisition, L.W. All authors have read and agreed to the published version of the manuscript.