# Robot Navigation Based on Human Trajectory Prediction and Multiple Travel Modes
## 3 Results and Analysis
### Trajectory Planning
#### 3.2.2 Validation Experiments with a Real Low-Density Crowd  
In this section, validation experiments on an actual robot system (e.g., [26]), a PatrolBot that is equipped with a Microsoft Kinect Sensor V2 and a laser (Sick LMS 200), are conducted to show the benefit of integration of human trajectory prediction. The Kinect V2 is used to recognize the pedestrians via the skeletal tracking algorithm [19].  
In the first scenario, the PatrolBot is commanded to navigate to the goal position and it encounters a pedestrian with a walking direction perpendicular to its path. The original TEB planner and our proposed CGP planner are utilized to generate the trajectory, respectively. Figure 9 shows the robot's navigation process of avoiding one pedestrian, \(p\). When using the TEB planner, the robot selects the trajectory of \(G(p)\) at first, as shown in Figure 9a. Then it switches to the trajectory of \(G_{b}(p)\) after a sharp return, as shown in Figure 9c,d. By contrast, when using the CGP planner, the switching process is smoother, as shown in Figure 9c,d. The average velocity of the robot with the TEB planner is 0.45 m/s, while that with the CGP planner is 0.67 m/s. In this scenario, the TEB planner generates trajectories with an evaluation function value of 17.5, and the CGP planner generates trajectories with an evaluation function value of 33.5.  
Figure 8: The trajectory planning process of avoiding five pedestrians with GP and CGP planners: (**a,b**) Avoiding \(p_{1}\), \(p_{2}\), and \(p_{3}\); (**c,d**) avoiding \(p_{5}\).The average velocity of the robot with the GP planner is 1.25 m/s, and the evaluation function value of the trajectory is 62.5; while the average velocity with the CGP planner is 1.39 m/s, and the evaluation function value of the trajectory is 69.5. This shows the necessity and effectiveness of trajectory planning with chasing cost judgment, which is proposed in our approach.  
Figure 9: The robot’s navigation process of avoiding one pedestrian with TEB and CGP planners: (**a**) the robot selects the trajectory of \(G_{p}(p)\); (**b,c,d**) a sharp return happens with TEB planner, however, no sharp return happens with CGP planner.  
In the second scenario, the PatrolBot encounters two pedestrians with walking directions perpendicular to its path. The original TEB planner and our proposed CGP planner are utilized to generate the trajectory, respectively. Figure 10 shows the robot's navigation process of avoiding two pedestrians, \(p_{1}\) and \(p_{2}\). When using the TEB planner, the robot selects the trajectory of \(G\)/\((p_{1})\) and \(G\)/\((p_{2})\) at first because it believes that the gap between the two pedestrians is enough to pass through, as shown in Figure 10a. Then, it switches to the trajectory of \(G_{b}(p_{1})\) after a sharp return, as shown in Figure 10c,d. By contrast, when using the CGP planner, there is no return in the switching process, as shown in Figure 10c,d. The average velocity of the robot with the TEB planner is 0.31 m/s, while that with the CGP planner is 0.53 m/s. In this scenario, the TEB planner generates trajectories with an evaluation function value of 10.5, and the CGP planner generates trajectories with an evaluation function value of 26.5.  
The results show the improvement of efficiency when integrating the TEB approach with human trajectory prediction.