Kejian Yan
Papers
2
Total Citations
40
H-Index
2
About
Kejian Yan is a rising researcher at the forefront of multi-robot systems and autonomous navigation, with a focus on bridging classical algorithms and modern learning-based approaches. His primary research areas include multi-agent pathfinding (MAPF), deep reinforcement learning, and sensor fusion for mobile robotics. Yan’s major contribution lies in his comprehensive review of graph-based MAPF solvers, which systematically charts the evolution from classical search-based methods to beyond-classical techniques, providing a crucial roadmap for researchers tackling large-scale coordination problems in robotics. This work has garnered 34 citations, establishing it as a key reference in the field. Additionally, Yan has advanced practical mobile robot navigation by integrating deep reinforcement learning with sensor fusion, addressing the limitations of traditional SLAM in dynamic environments—a paper that has already attracted 6 citations since its 2023 publication. His work is notable for its dual emphasis on theoretical synthesis and real-world applicability, making him a valuable voice for students and engineers seeking to understand both the foundations and frontiers of intelligent multi-robot coordination.
Research Focus
Key Achievements
Top Papers
- 1
- 2