## 4 Experiment and Results  
An environment based on the proposed functions was created to verify the efficacy of the proposed algorithm. The environment was a 9100 mm x 5100 mm square space with several pieces of office furniture located in the center, and inspection points were set in each corner of the room, as shown in Figure 9. At the beginning of the experiment, the robot was placed along a wall. Patrolling began when a user pressed the start button on the robot's HMI. The sequence of the patrol task is outlined in the following. Figure 8: Architecture of remote data backup and querying system. Figure 9: Environment used in patrol experiment. 1. From the start point until 21 s, the robot searched for a passable path and followed the resulting path. 2. At 31 s, the 1 st inspection point marker was detected by the robot, to which the robot approached. 3. At 44 s, the robot reached the 1 st inspection point. The RFID tag number was recorded by the robot, thereby completing the 1 st inspection point sign-in. 4. From 44 s to 54 s, the robot rotated CCW in search of its next route to follow. 5. From 54 s to 74 s, the robot moved from the 1 st to the 2 nd inspection point. 6. At 84 s, the robot located the 2 nd inspection point. The center of the marker image was not located in the target region of the image plane (between 310 and 330 pixels); therefore, the robot rotated until the image marker was near the center of the image plane (at 94 s), as shown in the figure. 7. At 94 s, the robot proceeded forward to sign-in at this inspection point at 101 s, whereupon it rotated and began patrolling from the 2 nd inspection point to the 3 rd inspection point (109 s to the 139 s). 8. The 3 rd and 4 th inspection points were identified at 159 s and the 203 s, respectively. After checking-in at the final (4 th) inspection point, the robot continued patrolling for approximately 30 seconds before halting. A color image and the corresponding depth information from the SVS of the above procedure are recorded throughout the experiment, as shown in Figures 10 and 11, and Video S1 (Supplementary Materials). The depth information \(\overline{D}\) of the ROI was also recorded to analyze the trajectory of the robot while on patrol. In order to fix the distortion caused by size-changing of FOV in different distance from SVS, the recorded data is transformed by Equation (2) and presented in the form of polar space, as shown in Figure 12. The FOV is depicted with a blue dashed line, and the threshold of depth \(d_{T}\) is shown with a red dashed line. In addition, the color scale is used to present the values of each \(\overline{d}_{n}\) in \(\overline{D}\). The analysis results are listed in the following:  
1. At 11 s and 129 s, the center of the section \(\overline{D}_{k}\) was near the origin of the polar space, which means that the robot was moving toward the center of the patrol path. 2. Between 21 s and 31 s, the robot was approaching the 1 st inspection point, as indicated by the decrease in recorded depth. 3. While the robot was signing in at the 1 st inspection point (at 44 s), the distance between the wall and the robot was less than the minimum distance required by the SVS for detection. Thus, no depth information appears in the polar space during this time. Similarly, the depth values disappeared at 94 s, 159 s, and 203 s, corresponding to the sign-in process at the 2 nd, 3 rd, and 4 th inspection points. 4. After signing in, the robot rotated CCW to search for a suitable path, whereupon the depth values increased considerably from the left side to the right side from 44 s to 64 s. This is similar to the records from 94 s to 119 s, 159 s to 173 s, and 193 s to 216 s.  
As shown in Figure 13, the robot followed the proposed algorithm in identifying patrol paths and signing in at inspection points. Figure 11: Depth information recorded from SVS during the patrol task. There were eight experiments made for patrolling; four of them were patrolling with inspection points placed, and the others did not.