# Robot Navigation Based on Human Trajectory Prediction and Multiple Travel Modes
## 3 Results and Analysis
### Navigation Based on Multiple Travel Modes and Predicted Pedestrians' Positions
#### 3.3.2 Validation Experiment in a Complex Medium-Density Crowd Environment  
To further test the performance of our proposed navigation strategy and trajectory planning algorithm in a complex medium-density crowd, we used the ROS packages for a 2D pedestrian simulator based on the social force model of [PERSON] et al. (2017) developed in the social situation-aware perception and action for cognitive robots (SPENCER) project ([PERSON] et al., 2018). The custom mobile robot is equipped with a front-facing stereo camera system, four RGB-D sensors, and a pair of 2D laser scanners ([PERSON] et al., 2018). In this scenario, the coordinates of the simulated pedestrians are known and sent to the robot system in real time, and the simulated robot uses the laser sensors only. A portion of the observed pedestrian trajectories are collected as the dataset to train the improved socially conscious model, and the performance of our proposed navigation approach is tested with the complex medium-density crowd environment. The ADE reduction percentage and FDE reduction percentage of our proposed improved socially conscious model is 6.2% and 7.9%, respectively, in this scenario. Figure 11: Performance comparison between planners with and without the follow mode. The green curves and lime curves represent the local trajectories and global path, respectively. (**a**,**b**,**c**) The trajectories generated in three positions with and without the follow mode. Figure 13a is an overall view of this scenario, and the regions in the two dotted line frames indicate the regions of Figure 13b,c-h, respectively. In the figure, the pedestrians connected by the yellow line represent identified groups, and the task of the robot is to reach the goal position from its current position. In Figure 10b, the robot detects that DATS is in a travelable state. The robot generates a trajectory with our proposed CGP planner, and passes through the crowds for the first time without collision. In Figure 10c, the robot finds that the followability evaluation function of target 1 is within 2, and it is also reachable judged with the method in Section 2.2. Therefore, it is confirmed that DATS is in smooth state, and target 1 is selected as the target to be followed and the robot switches to the high-speed-follow mode. In Figure 13d, during following the target 1, the robot predicts that the moving direction of target 1 is on the verge of deviating from the ideal moving direction, and the value of the followability evaluation function would increase to 420; at the same time, the followability evaluation function of the candidate low-speed-follow target 2 would be in the interval [2,12], but the ideal following target point is within the safe area of another pedestrian and, therefore, target 2 cannot be used as a followable target. Subsequently, the CGP planner does not find a feasible travel trajectory, and the robot determines that DATS is in the congested state and switches to the probing mode. In Figure 13e, the robot constantly monitors the traffic state of DATS while waiting. In Figure 13f, the robot finds a feasible trajectory and switches to the travelable mode, and finally passes through the crowd and reaches the final goal position. During the navigation, the robot has no collision with pedestrians. The average velocity is 0.3 m/s due to the long waiting time, and it crosses seven pedestrians' safe zones in total. The evaluation function value is \(-\)55. When the robot navigates without the probing mode, the average velocity is 0.15 m/s, and it crosses eight pedestrians'  
Figure 12: The robot’s navigation process with multiple travel modes and with the travelable mode only: (**a,b**) the robot explores trajectories randomly to detour the pedestrians ; (**c,d**) the robot collides with the wall; (**e,f,g**) the robot adjusts its trajectory again in the exploration process; (**h**) the robot reaches the goal position. safe zones totally. The evaluation function value is \(-72.5\).