Graph Relational Reinforcement Learning for Mobile Robot Navigation in Large-Scale Crowded Environments

Mobile robot autonomous navigation in large-scale environments with crowded dynamic objects and static obstacles is still an essential yet challenging task. Recent works have demonstrated the potential of using deep reinforcement learning to enable autonomous navigation in crowds. However, only considering the human-robot interactions results in short-sighted and unsafe behaviors, and they typically use hand-crafted features and assume the global observation range, leading to large performance declines in large-scale crowded environments. Recent advances have shown the power of graph neural networks to learn local interactions among surrounding objects. In this paper, the authors consider autonomous navigation task in large-scale environments with crowded static and dynamic objects (such as humans). Particularly, local interactions among dynamic objects are learned for better-understanding their moving tendency and relational graph learning is introduced for aggregating both the object-object interactions and object-robot interactions. In addition, local observations are transformed into graphical inputs to achieve the scalability to various number of surrounding dynamic objects and various static obstacle patterns, and the globally guided reinforcement learning strategy is introduced to achieve the fixed-sized learning model even in large-scale complex environments. Simulation results validate their generalizability to various environments and advanced performance compared with existing works in large-scale crowded environments. In particular, their method with only local observations performs better than the benchmarks with global complete observability. Finally, physical robotic experiments demonstrate their effectiveness and practical applicability in real scenarios.

• Record URL:
  • http://dx.doi.org/10.1109/TITS.2023.3269533
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2023, IEEE.
• Authors:
  • Liu, Zhe
  • Zhai, Yu
  • Li, Jiaming
  • Wang, Guangming
  • Miao, Yanzi
  • Wang, Hesheng
• Publication Date: 2023-8

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 8776-8787
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 24
  • Issue Number: 8
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Automated vehicle control; Crowds; Mobile robots; Neural networks; Safety
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01891204
• Record Type: Publication
• Files: TRIS
• Created Date: Aug 28 2023 9:19AM