Dong‐Ho Kang
Papers
1
Total Citations
7
H-Index
1
About
Dong-Ho Kang is a robotics researcher whose work centers on the control and locomotion of legged systems, particularly quadrupedal robots. His major contribution lies in advancing nonlinear model predictive control (NMPC) for dynamic locomotion, where he has developed formulations that jointly optimize base trajectories and foothold placements over finite horizons using simplified dynamics. A standout achievement is his 2022 paper on "Nonlinear Model Predictive Control for Quadrupedal Locomotion Using Second-Order Sensitivity Analysis," which has garnered 7 citations and demonstrates his ability to push the boundaries of real-time control by leveraging second-order sensitivity analysis for improved computational efficiency and robustness. This work addresses critical challenges in enabling agile, stable movement in complex environments, making it highly relevant for researchers in robotics and control theory. Kang’s research bridges theoretical optimization with practical robotic applications, offering tools that enhance the autonomy and adaptability of legged machines. His focus on computationally tractable yet powerful control strategies positions him as a contributor to the next generation of versatile, terrain-adaptive robots.
Research Focus
Key Achievements
Top Papers
- 1