Getting Robots Unfrozen and Unlost in Dense Pedestrian Crowds

Tingxiang Fan, Xinjing Cheng, Jia Pan, Pinxin Long, Wenxi Liu, Ruigang Yang, Dinesh Manocha

Research output: Contribution to journalArticlepeer-review

39 Scopus citations


Our goal is to navigate 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 robots and humans. Second, the robot takes advantage of its excellent local mobility to recover from its localization failure. In particular, it dynamically chooses to approach a set of recovery positions with rich features. To the best of our knowledge, our method is the first approach that simultaneously solves 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. Videos are available at

Original languageEnglish
Article number8606220
Pages (from-to)1178-1185
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number2
StatePublished - Apr 2019


  • Navigation in crowds
  • actor-critic collision avoidance

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


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