## 4 Results and Discussion
### Experimental Results
The distances between the empty green and filled red circles along the target person's trajectory indicate his/her disappearance at the corners as he/she continued walking toward the destination location. As presented in Figure 10b, the robot followed the target person to the destination location successfully when he/she walked naturally. Moreover, it correctly predicted the target's trajectory to recover him/her when he/she disappeared from the FoV of the robot at the corners twice, as shown in Figure 10c  
coordinate at the second corner based on the history of the target's path, as explained in Section 3.4. Figure 10d,e show the trajectories of the target and robot in the x and y directions, respectively, while the robot follows the person. The blue dotted curve represents the trajectory of the robot, and the green curve represents the trajectory of the target. The black and red dotted vertical lines represent the moments of the LOP and the re-tracking of the target, respectively. The yellow and red areas between the target and mobile robot trajectories respectively represent the distance between the trajectories. The \(-,+\) coordinates are relative to the origin of the map at the laboratory. In general, if we project the trajectories of the person and the robot in the test environment, the target moved from the laboratory to the end of the corridor in the \(+(x,y)\) coordinates (Figure 8). The target person started walking approximately along the \(+x\) coordinate in the laboratory, then turned left along the \(+y\) coordinate at 9.5 s (Figure 10e). The robot lost the person at 12.9 s when the person was in the corridor and the robot was still in the laboratory. The robot recovered the person at 17.6 s, when both the robot and the person were in the corridor after they passed through the first corner. The person continued walking along the \(+y\) coordinate, then turned left along the \(+x\) coordinate at 30.7 s (Figure 10d). The robot lost the person at 32.1 s and recovered the person at 37.7 s after he/she passed through the second corner (Experiment 9 in Table 1). Figure 10f shows a comparison of the trajectory predictions from the proposed method, LasCV, HR, and VBLR. The stored positions of the target were used as the input data to the models, and the second-order polynomial was applied for all the models at Corner 2. The black scattered circles represent the stored target positions, which were estimated  
Figure 10: Person and robot movement trajectories in a realistic scenario. (**a**) Recovery parameter comparison for the two corners. (**b**) Real-world person and robot trajectories. (**c**) Predicted trajectories of the target and the stored positions of two corners. (**d**) Person and robot trajectories along the \(x\) coordinate. (**e**) Person and robot trajectories along the \(y\) coordinate. (**f**) Target trajectory prediction using the proposed method, LasCV, HR, and VBLR. during the tracking state by the camera, and the last circle represents the LOP. The colored dotted line denotes the yellow wall between the test environment and the other laboratories (Figures 8 and 9e-h). The wall is parallel to the x axis, and the distance between the wall and the original reference along the y axis was 15.2 m. The empty blue circles at the vertices of the predicted trajectories are the last predicted positions, which served as the main goals. The small filled red circles represent the predicted positions in the known map, which served as the secondary goals. The LPP \((x_{n-1},y_{n-1})\) that was predicted by the LasCV, HR, and VBLR models fell outside of the boundaries of the known map. The main goal of the robot, therefore, became invalid, and the robot moved instead to the secondary goal of the nearest position to the boundary of the known map. However, it was difficult for the robot to recover the target at the secondary goal owing to the short distance between the secondary goal and the LOP.