Sang Su Lee
Papers
4
Total Citations
141
H-Index
3
About
Sang Su Lee is a leading researcher in mobile robotics, specializing in robust localization and obstacle avoidance for autonomous systems operating in uncertain environments. His most impactful work, the "Finite Distribution Estimation-Based Dynamic Window Approach" (112 citations), introduces a novel algorithm that overcomes the limitations of traditional dynamic window methods by integrating finite memory filtering (FMF), enabling reliable obstacle avoidance in unknown dynamic settings. Lee has further advanced the field with his "Improved Nonlinear Finite-Memory Estimation Approach for Mobile Robot Localization" (19 citations), which addresses performance degradation in Kalman and particle filters caused by disturbances or missing measurements. His research extends to multi-robot systems, as demonstrated in "Distributed Finite Memory Estimation From Relative Measurements for Multiple-Robot Localization" (7 citations), which enhances localization accuracy in wireless sensor networks under harsh conditions like sensor failures. Additionally, his "Novel Mobile Robot Localization Method via Finite Memory Filtering Based on Refined Measurement" (3 citations) pioneers a refined measurement approach that improves pose estimation over conventional infinite impulse methods. Collectively, Lee’s contributions provide computationally efficient, resilient solutions for real-world robotics, making his work essential for researchers tackling autonomous navigation in complex, unpredictable environments.
Research Focus
Key Achievements
Top Papers
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