# Robot Navigation Based on Human Trajectory Prediction and Multiple Travel Modes
## 3 Results and Analysis
### Trajectory Planning
#### 3.2.1 Validation Experiment with Simulated a Low-Density Crowd
(3) The TEB planner selects the trajectory of \(G_{f}(p_{3})\), while the TEBP planner first selects the trajectory of \(G_{f}(p_{3})\), and after running for a distance, it switches to \(G_{b}(p_{3})\), and this results in a return in this switching process, as shown in Figure 7d. (4) The TEB planner first selects the trajectory of \(G_{f}(p_{4})\), and after driving for a distance, it switched to \(G_{b}(p_{4})\), and this switching process results in a return; while the TEBP planner realizes smooth switching of this process, as shown in Figure 7e. The green dots in Figure 7 are the sampling points to generate new trajectories, and the points are continuously sampled in parallel during the navigation. The average velocity of the robot with the TEB planner is 1.12 m/s, while that with the TEBP planner is 1.21 m/s. In this scenario, the TEB planner generates trajectories with an evaluation function value of \(-\)54, and the TEBP planner generates trajectories with an evaluation function value of 50.5. This shows that our proposed TEB approach with pedestrian trajectory prediction (TEBP) performs better. Figure 6: Obstacle avoidance trajectories generated based on different pedestrian position predicting models: (**a**) the constant-speed model; (**b**) the improved socially conscious model. In the third scenario, there are five pedestrians, which are numbered \(p_{1}\) to \(p_{5}\) from left to right. Their velocities are 1.5 m/s, 1.4 m/s, 1.4 m/s, 1.2 m/s, and 1.2 m/s, respectively. The planner consisting of a global path planner and a TEBP local planner is denoted as the \"GP\" planner, and the GP planner with chasing cost judgment is denoted as the \"CGP\" planner. The robot with CGP planner detects the feasibility of the trajectories according to the method in Section 3.2.2. When there is no feasible trajectory, the robot will enter the probing mode. Figure 8 shows the trajectory planning process with the GP and CGP planners for the robot to avoid the five pedestrians, which is as follows: The GP planner starts with selecting the trajectory of \(G_{j}(p_{1})\), as shown in Figure 8a, after the robot travels for a distance, the GP planner switches to the trajectory, \(G_{b}(p_{1})\), which results in a return, as shown in Figure 8b; while the CGP planner judges that trajectory of \(G_{j}(p_{1})\) is unfeasible, and there is no feasible trajectory of \(G_{b}(p_{1})\) found, then the robot enters the probing mode, and sets the point 0.7 m away from \(p_{1}\) along \(L\) as the temporary goal point of the robot, as shown in Figure 8a. When the feasible global trajectory of \(G_{b}(p_{1})\) and \(G_{b}(p_{2})\) is found, the robot enters the travelable mode, as shown in Figure 8b. In Figure 8c, the GP planner selects the trajectory of \(G_{j}(p_{5})\). After running for a distance, it switches to the trajectory of \(G_{b}(p_{5})\), and the global path remains unchanged, as shown in Figure 8d. However, when the CGP planner judges that the trajectory of \(G_{j}(p_{5})\) is unfeasible, it switches to the trajectory of \(G_{b}(p_{5})\), as shown in Figure 8c,d. Figure 7: The trajectory planning process of avoiding four pedestrians with timed-elastic-band (TEB) and TEB with pedestrian trajectory prediction (TEBP) planners: (**a**) Initial state; (**b**) avoiding \(p_{1}\); (**c**) avoiding \(p_{2}\); (**d**) avoiding \(p_{3}\); and (**e**) avoiding \(p_{4}\). The red dots represent the predicted pedestrians’ positions, and the green dots are sampling points to generate new trajectories. The circle in grey is the robot.