Yong Liu
Papers
2
Total Citations
89
H-Index
2
About
Yong Liu is a researcher whose work spans robotics, autonomous systems, and computer vision, with a particular focus on the complex challenges of multi-agent coordination and spatial awareness. His most significant contribution, the 2021 paper "CL-MAPF: Multi-Agent Path Finding for Car-Like Robots with Kinematic and Spatiotemporal Constraints," has garnered 83 citations, establishing him as a notable voice in the multi-agent path finding (MAPF) community. This work tackled a critical real-world gap in MAPF research by incorporating the physical constraints of car-like robots — including turning radii and velocity limitations — alongside spatiotemporal planning, making the approach far more applicable to practical autonomous vehicle deployment scenarios. Earlier in his career, Liu also explored foundational problems in machine perception, contributing an image-based method for self-positioning and orientation of moving platforms (2012), reflecting a consistent interest in enabling machines to understand and navigate their environments intelligently. His research bridges theoretical algorithmic development with tangible engineering applications, making meaningful contributions to the growing fields of autonomous robotics and intelligent transportation systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Image-based self-position and orientation method for moving platform6 citations · 2012