Papers
16
Total Citations
222
H-Index
7
About
Soo Jeon is a robotics and control systems researcher whose work spans autonomous mobile robots, state estimation, and intelligent sensing. His foundational contribution, the Kinematic Kalman Filter (KKF) for robot end-effector sensing (2009, 51 citations), addressed a long-standing challenge in industrial manipulator control by enabling accurate end-effector motion estimation despite kinematic errors and joint flexibility — a problem previously constrained by sole reliance on motor encoders. His research has since expanded into mobile robotics, where his work on Model Predictive Control without terminal constraints for holonomic robots (2020, 41 citations) and its path-following extension (2022, 25 citations) offers computationally elegant solutions for real-time robot navigation. His 2018 work on resonance-based snake robots with parallel elastic actuators (33 citations) demonstrates a creative approach to energy-efficient locomotion design. More recently, Jeon has tackled perception and localization challenges through multi-sensor fusion using moving horizon estimation (2021, 23 citations), self-supervised stereo matching, and deep learning-based occupancy grid mapping. Across these contributions, his research consistently bridges rigorous control theory with practical autonomous systems engineering, making him a versatile and impactful voice in modern robotics research.
Research Focus
Key Achievements
Top Papers
- 1Kinematic Kalman Filter (KKF) for Robot End-Effector Sensing51 citations · 2009
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