Papers
1
Total Citations
19
H-Index
1
About
Yoonki Cho is a robotics researcher whose work focuses on real-time motion planning, inverse kinematics, and collision avoidance for robotic manipulators. His most notable contribution is the development of RCIK (Real-Time Collision-Free Inverse Kinematics), a novel framework that integrates a collision-cost prediction network with optimization-based IK to generate safe, accurate joint configurations in environments with both static and dynamic obstacles. This work, published in 2021 and cited 19 times, addresses a critical challenge in robotics: enabling robots to perform consecutive six-degree-of-freedom commands without collisions in real time. Cho’s approach stands out for its ability to balance computational efficiency and precision, making it highly relevant for applications in manufacturing, human-robot interaction, and autonomous systems. By combining deep learning with traditional optimization, he has advanced the state of the art in reactive motion generation. His research is particularly valuable for students and engineers seeking robust solutions for safe robot operation in cluttered or unpredictable settings.
Research Focus
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Top Papers
- 1