Tingxiang Fan
Papers
18
Total Citations
794
H-Index
12
About
Tingxiang Fan is a leading researcher in multi-robot systems and autonomous navigation, with a focus on deep reinforcement learning for safe and efficient robot motion in complex, dynamic environments. His most impactful work, "Distributed multi-robot collision avoidance via deep reinforcement learning for navigation in complex scenarios" (322 citations), pioneered decentralized collision-avoidance policies that enable robots to navigate without full knowledge of others' intentions. Fan's contributions extend to socially-aware robotics, including autonomous social distancing using quadruped robots (71 citations) and navigation in dense pedestrian crowds (68 citations), addressing the "frozen robot" problem. He also developed CrowdMove, a mapless navigation framework for crowded scenarios, and DynamicFilter for removing dynamic objects in urban environments. His work on safe human-robot collaboration and natural language-guided navigation demonstrates a commitment to practical, real-world deployment. With over 700 total citations, Fan's research has significantly advanced the field of multi-agent navigation, offering scalable solutions for everything from warehouse logistics to pandemic response.
Research Focus
Key Achievements
Top Papers
- 1
- 2Autonomous Social Distancing in Urban Environments Using a Quadruped Robot71 citations · 2021
- 3
- 4Getting Robots Unfrozen and Unlost in Dense Pedestrian Crowds68 citations · 2019
- 5CrowdMove: Autonomous Mapless Navigation in Crowded Scenarios47 citations · 2018
- 6
- 7
- 8Safe Navigation With Human Instructions in Complex Scenes33 citations · 2019
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