# Robot Navigation Based on Human Trajectory Prediction and Multiple Travel Modes
## 1 Introduction
However, the planner has not been integrated with the prediction model of human trajectory, which results in an unintelligent manner in which robots respond to human motion. When the pedestrian movement speed is relatively high, or the space among pedestrians is relatively small, the generated trajectory of topology in front of the pedestrian may not be feasible. Online decision making for robots in crowded and dynamic environments is also a fundamental challenge. The approaches fall into two major groups. (1) The first one models the decision making and planning as a partially observable Markov decision process (POMDP) [16]. [PERSON] et al. [17] applied a state-of-the-art approximate online POMDP planning algorithm to autonomous driving in a complex, dynamic environment. Until now, there have been few reports on navigation in a medium-density crowd, the reason may be that the computational cost is still high. (2) The second approach uses a cooperative collision avoidance model between humans and the robot to conduct cooperative planning for robots, such as works [7; 18]. However, the modeling process is not a trivial work, and an alternative simple and practical navigation strategy is required. This paper aims to predict the movement of pedestrians, and then solve the navigation problem of robots crossing low-density and medium-density crowds. First, an improved socially conscious model is used to learn and predict the pedestrian's future trajectory, in which the effect of other pedestrians' moving direction is considered, and the effect of a pedestrian's own history trajectory on the pedestrian's future trajectory is enhanced. Second, local trajectory for obstacle avoidance optimization based on the predicted pedestrian trajectory and chasing cost judgment is performed to generate safer and more efficient trajectories. Third, we have designed the five following travel modes for robot navigation in different traffic states: Free mode, high-speed-follow mode, travelable mode, low-speed-follow mode, and probing mode. In the two following modes, the followability and reachability tests are conducted and evaluated and the optimal following target is selected; in the probing mode, a short trajectory ending at the boundary of the safe space of the nearest pedestrian ahead is planned to deal with the congested traffic state, which happens often in medium-density crowds. Finally, the improved socially conscious model is tested with public datasets, and our proposed navigation approach is validated in simulated low-density and medium-density crowded scenarios. The results show that our proposed human trajectory prediction model and robot navigation approach outperform the state-of-art methods, and a robot can navigate safely and smoothly in a crowded unstructured environment with our proposed navigation method. In summary, we make the following contributions:  
* We present an improved socially conscious model in which a pooled layer between the current pedestrian trajectory sequence input and the pedestrian position estimation layer is added, which models pedestrian trajectories more accurately;
* we present a novel online path planning algorithm that integrates TEB with pedestrians' predicted movement and feasibility detection, and is capable of generating efficient and safe trajectories in changing crowed space;
* we present multiple modes of travel to guide the robot to find the optimal navigation strategy under changing traffic situations in a crowed area; and
* we demonstrate the proposed algorithms with an experiment in eight scenarios. The paper is structured as follows: We first present the related work in Section 1. In Section 2, we propose human trajectory predictions with an improved socially conscious model, and the online trajectory planning method for robots based on multiple modes of travel is described. In Section 3, our proposed approach is validated in several low-density crowded scenarios and a medium-density scenario with simulation, and the performance is compared with the state-of-art methods.