Papers
7
Total Citations
54
H-Index
4
About
Jeongryul Kim is a robotics researcher whose work bridges bio-inspired locomotion, surgical robotics, and intelligent control systems. His research centers on three key areas: legged locomotion mechanisms, hyper-redundant manipulators for minimally invasive surgery, and deep learning–driven robotic perception. Kim’s most cited work, “Design of a slider-crank leg mechanism for mobile hopping robotic platforms” (25 citations), lays foundational principles for efficient, high-speed robotic hopping. He further advanced bio-inspired design with a lizard-inspired robot that achieves stable bipedal running through S-shaped lateral body motions, a notable contribution published in 2019. In surgical robotics, Kim has developed a novel arthroscopic pre-curved cannula that combines flexibility with high stiffness, and a hand-held non-robotic device that compensates for wire length in unpredictable paths—improving precision at low cost. His recent work employs deep neural networks for force estimation in hyper-redundant manipulators (2024, 8 citations) and visual feedback for nasopharyngeal swab sampling (2023), demonstrating a commitment to translating robotics into real-world clinical impact. With a growing portfolio of over 50 citations, Kim is establishing himself as a versatile innovator at the intersection of mechanism design, control, and medical application.
Research Focus
Key Achievements
Top Papers
- 1Design of a slider-crank leg mechanism for mobile hopping robotic platforms25 citations · 2013
- 2DNN-Based Force Estimation in Hyper-Redundant Manipulators8 citations · 2024
- 3A New Lizard-Inspired Robot With S-Shaped Lateral Body Motions7 citations · 2019
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