# Robot Navigation Based on Human Trajectory Prediction and Multiple Travel Modes
## 1 Introduction  
Robots are expected to coexist with humans and perform a variety of tasks. Navigation skills are essential for autonomous mobile robots. However, robot navigation in crowded, dynamic environments in a safe, efficient, and socially compliant manner is a challenging problem [1]. To achieve this, robots need to not only accurately predict motions of pedestrians, but also analyze the developing traffic situations [2], and plan their own paths or trajectories accordingly. The purpose of this is to improve pedestrian movement prediction, include prediction based on a social model to robot trajectory planning, and propose a novel navigation strategy for robots in a dynamic environment. The techniques for prediction of the pedestrians' trajectory prediction have attracted much attention from academia. One method relies on closed-form solutions for Bayesian filtering, such as Kalman filters (KF) [3], including the interacting multiple model-Kalman filter (IMM-KF) [3], which uses KF extensions to multiple linear dynamical models, and unscented KF [4], which uses KF extensions to nonlinear dynamical models. Recently, [PERSON] et al. [5] developed a parallel interacting multiple model-unscented Kalman filter (PIMM-UKF) approach to estimate human motion states,and then predicted the position and velocity of humans for a finite horizon. [PERSON] et al. [6] combined pedestrian intention recognition with path prediction, through an interacting multiple model filter in combination with a latent-dynamic conditional random field model. However, most of these research works are limited by independent models that fail to capture the complex interactions between the humans in the crowd. An alternative and recent method utilizes learning techniques to model the joint distribution of future trajectories of interacting agents based on a spatially local interaction model. [PERSON] et al. [7] proposed an interactive Gaussian process approach, whose kernels were used to model human dynamics, to capture cooperative collision avoidance between humans and a robot. [PERSON] et al. [8] proposed long-short term memory (LSTM) networks with \"social\" pooling layers, which learned general human movements and predicted their future trajectories. Recently, we proposed a socially conscious model considering the added features that affect the pedestrian's future trajectory, such as the walking direction of other pedestrians [9]. However, these approaches do not carefully distinguish the effects of a pedestrian's own history trajectory, and that of others, to the pedestrian's future trajectory, and this may hinder the improvement of model accuracy. Developing efficient online path planning algorithms to generate robots' safe and smooth trajectory is a basic problem in robot navigation. Safe trajectory generation is a very active field in mobile robotics and there are many recent contributions. [PERSON] [10] and [PERSON] [11] reviewed the state-of-the-art in smooth trajectory generation with comparisons in terms of kinematic feasibility and safe path generation recently. Additionally, the approaches that are available with robot operating system (ROS) integration are worth mentioning. One approach called the dynamic window approach (DWA) is adopted extensively for mobile robot navigation [12]. Recently, [PERSON] et al. [13; 14] presented an online trajectory planning and optimization algorithm called the timed-elastic-band (TEB) approach, which had been generalized to an efficient time-optimal model predictive control approach for dynamic systems. Furthermore, to overcome the shortcomings of traditional local planners that are unable to transit across obstacles, they proposed a state-of-the-art planner that switched to the current globally optimal trajectory among the candidate trajectories of distinctive topologies maintained and optimized in parallel in the case of dynamic environments [15].