## 4 Experiments
### Infrared Docking  
When the robot approaches the charging station and enters the close-range docking mode, the infrared emitters on the charging station start emitting 38 KHz infrared carrier signals of varying durations. The infrared receivers on the robot identify these signals to determine its position and orientation relative to the charging station. The docking process is shown in Figure 12.  
If the left-side receiver receives a signal from emitter A and the right-side receiver receives a signal from emitter B, the robot adjusts its movement according to the predefined logic to ensure it faces the charging interface of the charging station. Once the robot is aligned with the charging station, it will continue moving forward and make contact with the charging port to complete the docking. Throughout the process, the robot continuously monitors the changes in infrared signals and adjusts its direction and position to ensure successful docking.  
Figure 11: Navigation paths starting from three different positions: (Top row) LiDAR-only mapping, (Bottom row) LiDAR and visual mapping. (**a**) LiDAR mapping (Start 1). (**b**) LiDAR mapping (Start 2). (**c**) LiDAR mapping (Start 3). (**d**) LiDAR and visual mapping (Start 1). (**e**) LiDAR and visual mapping (Start 2). (**f**) LiDAR + Visual mapping (Start 3).  
Figure 12: Real infrared docking process.  
The experiments were conducted to evaluate the performance of the AGV in terms of map construction, path planning, navigation, and automatic docking under different sensor configurations. The tested configurations include infrared sensors, LiDAR, visual systems, and a combination of these sensors. The AGV was tasked with autonomously navigating from various starting positions to a charging station, avoiding obstacles along the way, and performing an automatic docking maneuver at the station. Table 3 summarizes the results of the tests, showing the advantages of sensor fusion over single-sensor methods.  
The mapping process was initiated using three different sensor configurations: LiDAR with the gmapping algorithm, a visual system with the RTAB-Map, and a fusion of LiDAR and visual data with the RTAB-Map. LiDAR-based mapping using gmapping resulted in accurate and robust maps, but visual-only mapping struggled in low-light conditions and narrow spaces, leading to incomplete maps. The fusion of LiDAR and visual systems provided superior map quality, with fewer blind spots and more detailed environmental information. Path planning and navigation tests showed that the AGV using sensor fusion navigated more efficiently, as reflected in the reduced path planning distance and time to reach the target. Additionally, the AGV was able to avoid obstacles more effectively compared to using either LiDAR or visual systems alone. Finally, the automatic docking task demonstrated that relying solely on infrared or visual sensors led to higher docking errors, while the multi-sensor fusion system significantly improved docking precision and success rate. The experimental results clearly demonstrate the benefits of sensor fusion, particularly when operating in complex indoor environments where lighting conditions and obstacle configurations vary.