Abstract

We aim to enable a mobile robot to navigate through environments with dense crowds, e.g., shopping malls, canteens, train stations, or airport terminals. In these challenging environments, existing approaches suffer from two common problems: the robot may get frozen and cannot make any progress toward its goal; or it may get lost due to severe occlusions inside a crowd.

Here we propose a navigation framework that handles the robot freezing and the navigation lost problems simultaneously. First, we enhance the robot's mobility and unfreeze the robot in the crowd using a reinforcement learning based local navigation policy developed in our previous work, which naturally takes into account the coordination between the robot and the human. Secondly, the robot takes the advantage of its excellent local mobility to recover from its localization lose. In particular, it dynamically chooses to approach a set of recovery positions that have rich features. To the best of our knowledge, our method is the first approach solving simultaneously both the freezing problem and the navigation lost problem in dense crowds. We evaluate our method in both simulated and real-world environments and demonstrate that it outperforms the state-of-the-art approaches.