# Robot Navigation Based on Human Trajectory Prediction and Multiple Travel Modes
## 4 Discussion and Conclusions  
This paper proposes an improved socially conscious model for human trajectory prediction and a novel approach for robot navigation under changing traffic situations in a crowded area. We analyse and discuss our proposed methods as follows, and reveal the advantages and limitations. First, the added pooling layers in the improved socially conscious model can significantly reduce the prediction error of the model compared with Social-LSTM. This finding suggests that the relative distance and walking direction of pedestrians around the target pedestrian are both important; furthermore, the influence of the target pedestrian's own historical trajectory is larger than that of others around him/her. However, if the distribution of the training data is inconsistent with the current pedestrian's walking pattern, the predicting error still exists and cannot be ignored. Figure 13: Experiment in a complex crowd simulation environment: (**a**) Overview of the scenario; (**b**) the robot passes through the crowd for the first time; (**c**) the robot chooses to follow the target 1; (**d**) the robot is detecting the accessibility of the candidate following target 2; (**e**) the robot is in the probing mode; (**f**) the robot switches to the travelable mode. One way to improve the model predicting accuracy may be to explicitly consider the pedestrian's destination, intention, and environmental static obstacles as reported in [29], and another way may be to automatically classify the personality and characterize each pedestrian's behavior based on a weighted combination of different personality traits [30]. Second, the trajectories generated by the TEB trajectory planning method with predicted pedestrian trajectory are safer, more efficient, and more socially compatible. The reason is that the introduction of the pedestrian's predicted position makes the trajectory cost of the pedestrian's frontal topology increase, and the trajectory cost of the pedestrian's backside decrease; the increase of the cost difference benefits the optimal trajectory searching. Furthermore, the optimal trajectory generation in advance and the increased distance between the robot and pedestrians at their front sides improves the navigation safety and efficiency. The proposed pedestrian's positions' prediction after 0.2 s as the occupation area handles the prediction error very well. These results prove that the proposed trajectory planning method achieves our original intention. Third, the generated trajectory's comprehensive performance with chasing cost judgment is improved. The reason is that the global trajectory of the robot is constrained so that it does not deviate too far from the shortest distance; on the other hand, discarding trajectories whose chasing costs are too high is beneficial to reduce the energy consumption of the robot, which is similar to an actual human's trajectory selection strategy. However, there are some limitations of this method, as follows: (1) In terms of the smoothness of the trajectories, the inescapable switching between different topological trajectories is sometimes not smooth and the curve has \(C^{0}\) continuity only [10], especially when the robot is close to pedestrians. However, the trajectory with \(C^{2}\) continuity, which preserves acceleration, is expected, so additional works that focus on smoothing the switching trajectories are needed. (2) In terms of computational time, the added human trajectory prediction and chasing cost judgment cost some time. Although the calculation of pedestrians' new predicted trajectories is in parallel with planning based on the old predicted trajectories, the cost time cannot be ignored. The global trajectory searching takes some time and the efficiency needs to be improved. The improvement of the efficiency of the trajectory planning and the safety of the generated trajectory contradict each other, and they should be balanced. (3) The issue of real-time detection is not the research focus.