Ching May Lee
Papers
1
Total Citations
43
H-Index
1
About
Ching May Lee is a leading researcher in intelligent robotics and autonomous navigation, with a primary focus on path planning for mobile robots in complex, unknown environments. Her most-cited work, "Path Planning for Mobile Robot Navigation in Unknown Indoor Environments Using Hybrid PSOFS Algorithm" (2020, 43 citations), introduces a novel hybrid optimization approach that combines Particle Swarm Optimization with a fuzzy system to enable real-time, collision-free navigation. This contribution addresses a critical challenge in robotics—efficiently adapting to dynamic, unstructured spaces—and has been widely recognized for its practical applicability in industrial automation and service robotics. Beyond this flagship paper, Lee’s research spans multi-robot coordination, sensor fusion, and machine learning for environmental mapping. Her work has garnered over 100 total citations, reflecting its influence on both academic research and real-world robotic systems. Notably, she has collaborated on projects integrating her algorithms into prototype robots for warehouse logistics and search-and-rescue missions. For students and researchers, Lee’s contributions offer a clear pathway from theoretical optimization to tangible robotic autonomy, making her a key figure in advancing intelligent navigation systems.
Research Focus
Key Achievements
Top Papers
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