Youngwan Cho

About

Youngwan Cho is a robotics researcher whose work bridges adaptive control, autonomous navigation, and intelligent manipulation. His most influential contribution is a direct adaptive fuzzy control framework for nonlinear systems, applied to robot manipulator tracking control—a paper that has garnered 29 citations and laid groundwork for handling uncertain dynamics without precise mathematical models. Cho also developed the home Mess-Cleanup Robot McBot, an early autonomous cleaning system that advanced beyond simple vacuuming to tackle general household mess, earning 16 citations and demonstrating practical robotics deployment. His research extends to quadruped locomotion, where he used genetic algorithms to optimize gait speed and stability, achieving improved performance in physics-based simulations. In path planning, Cho introduced the Retrieval RRT Strategy, merging support vector machines with rapidly-exploring random trees to adapt to changing environments. He also contributed to moving object recognition and tracking by fusing SURF and Lucas-Kanade algorithms. Across these works, Cho has shown a consistent focus on making robots more adaptive, autonomous, and capable in real-world settings—from factory floors to living rooms.

Research Focus

Key Achievements

Top Papers

1. 1
   A Direct Adaptive Fuzzy Control of Nonlinear Systems with Application to Robot Manipulator Tracking Control

   29 citations · 2007
2. 2
   A study on development of home Mess-Cleanup Robot McBot

   16 citations · 2008
3. 3
   Tracking control of a robot manipulator using a direct model reference adaptive fuzzy control

   5 citations · 2003
4. 4
   Automatic Gait Generation for Quadruped Robot Using GA with an Enhancement of Performance

   3 citations · 2008
5. 5
   Path planning of a Robot Manipulator using Retrieval RRT Strategy

   3 citations · 2007
6. 6
   A Study On Algorithm Fusion For Recognition And Tracking Of Moving Robot

   2 citations · 2012

Key Collaborators

Contact & Links