Man-Seak Kim
Papers
1
Total Citations
5
H-Index
1
About
Man-Seak Kim is a robotics researcher whose work focuses on bipedal locomotion and humanoid robot control, particularly in complex environments like stair navigation. His most-cited paper, "Optimal Trajectory Generation for Walking Up a Staircase of a Biped Robot Using Genetic Algorithm" (2009, 5 citations), introduces a novel approach to generating stable, efficient trajectories for humanoid robots ascending stairs. By combining blending polynomial techniques with genetic algorithms, Kim proposed and simulated four distinct walking schemes, leveraging advanced kinematics to optimize step patterns and balance. This work addresses a critical challenge in humanoid robotics—adapting bipedal motion to uneven terrain—and demonstrates a practical method for improving robot autonomy in real-world settings. Though his citation count is modest, Kim’s contributions are notable for their innovative integration of evolutionary computation with motion planning, laying groundwork for more adaptive and robust legged robots. His research holds particular value for students and engineers exploring trajectory optimization, bio-inspired robotics, or the intersection of AI and mechanical design in humanoid systems.
Research Focus
Key Achievements
Top Papers
- 1