Sung-Min Han
Papers
5
Total Citations
53
H-Index
4
About
Sung-Min Han is a robotics researcher whose work bridges autonomous navigation and visual servo control, with particular expertise in mobile robot path planning and image-based robot manipulation. His research addresses two interconnected challenges in modern robotics: enabling mobile robots to navigate complex environments intelligently and equipping robot manipulators with robust visual feedback control under real-world uncertainties. Han's most significant contribution lies in developing circular path planning algorithms for mobile robot obstacle avoidance, a body of work spanning multiple publications from 2008 to 2009. These algorithms cleverly leverage monocular camera vision combined with ultrasonic sensing to determine optimal avoidance trajectories based on detected obstacle position and size — an elegant, computationally efficient approach that improved both processing speed and noise immunity in real-time applications. In parallel, Han made notable strides in visual servo control, designing robust controllers capable of compensating for uncertainties in image Jacobian estimation and robot dynamics — critical challenges when camera parameters and payload conditions vary unpredictably. His 2010 paper on robust visual servo control stands as his most cited work, accumulating 22 citations. Collectively, Han's research has meaningfully advanced practical, uncertainty-tolerant robotics systems applicable across autonomous vehicles and industrial manipulation.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Mobile robot navigation using circular path planning algorithm9 citations · 2008
- 4Mobile Robot Navigation Using Circular Path Planning Algorithm5 citations · 2009
- 5
Key Collaborators
Related papers
- Mobile robot navigation using circular path planning algorithm
- Mobile robot navigation by circular path planning algorithm using camera and ultrasonic sensor
- Mobile Robot Navigation Using Circular Path Planning Algorithm
- Mobile robot vavigation using a vision based approach
- Experimental Motion Planning and Control for an Autonomous Nonholonomic Mobile Robot
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