###### Abstract  
Automated guided vehicles (AGVs) play a critical role in indoor environments, where battery endurance and reliable recharging are essential. This study proposes a multi-sensor fusion approach that integrates LiDAR, depth cameras, and infrared sensors to address challenges in autonomous navigation and automatic recharging. The proposed system overcomes the limitations of LiDAR's blind spots in near-field detection and the restricted range of vision-based navigation. By combining LiDAR for precise long-distance measurements, depth cameras for enhanced close-range visual positioning, and infrared sensors for accurate docking, the AGV's ability to locate and autonomously connect to charging stations is significantly improved. Experimental results show a **25%** increase in docking success rate (from **70%** with LiDAR-only to **95%**) and a **70%** decrease in docking error (from **10 cm** to **3 cm**). These improvements demonstrate the effectiveness of the proposed sensor fusion method, ensuring more reliable, efficient, and precise operations for AGVs in complex indoor environments. automated guided vehicles multi-sensor fusion; autonomous navigation; automatic recharging +
Footnote †: journal: Applied  
surface of the charging station is regularly covered with materials of different reflective intensities, enabling quicker detection of the charging station's location. Therefore, the charging station is usually designed to be relatively large. In this paper, LiDAR and cameras are used for autonomous navigation [11; 12; 13; 14] to complete the task of remotely finding the charging station, combined with infrared sensors for close-range docking to achieve automatic recharging. First, LiDAR provides high-precision distance and angle measurements, effectively detecting and avoiding obstacles, giving the robot strong autonomous navigation capabilities in complex environments. The camera supplements the LiDAR by providing visual information, recognizing and locating feature objects in the environment, offering a more comprehensive environmental perception. Infrared sensors are employed for the recharging task, guiding the robot to approach the charging station from a distance, addressing the issue of long-range precise docking that is difficult to achieve solely by vision and LiDAR [15; 16; 17]. Moreover, infrared sensors are not affected by lighting changes, enabling stable operation under different lighting conditions. Given these issues, the work presented in this paper focuses on addressing the limitations of traditional LiDAR systems, particularly their blind spots in near-field detection and the restricted range of vision-based navigation. This study employs a multi-sensor fusion approach that combines LiDAR and cameras to enhance the robot's ability to locate the charging station. The LiDAR generates a detailed map by scanning the environment, while the camera provides complementary visual information, enabling the robot to recognize feature objects and accurately determine the position and posture of the charging station. The mapping and navigation algorithm leverages this integrated information to plan the optimal path, effectively guiding the robot to the charging station. Throughout the navigation process, the LiDAR continuously monitors the environment in real time, identifying and avoiding obstacles to ensure safe passage. As the robot approaches the charging station, it transitions to a close-range infrared docking mode. Infrared transmitters installed on the charging station create multiple signal zones that the robot's infrared receivers can detect. Based on the received signal strength and direction, the robot adjusts its posture to achieve precise docking. By integrating these advanced sensing capabilities, the robot can efficiently execute autonomous navigation and automatic recharging tasks, significantly improving the reliability and operational duration of the robotic system.