Papers
2
Total Citations
12
H-Index
2
About
Yeong Jae Kim is a robotics researcher whose work bridges autonomous navigation and human-robot interaction. His primary research areas include deep learning-based motion planning, autonomous mobile robotics, and biosignal-driven control systems. Kim’s most notable contribution is his work on **Deep Learning-Based NMPC for Local Motion Planning of Last-Mile Delivery Robot** (2022), which has garnered 9 citations. This research addresses a critical challenge in autonomous navigation: enabling mobile robots to safely and efficiently navigate dynamic, unpredictable environments by integrating deep learning with nonlinear model predictive control (NMPC). By improving how robots predict and respond to scene evolution, Kim’s work advances the feasibility of last-mile delivery robots in real-world settings. Earlier, he explored human-robot collaboration through **Line tracking control of a mobile robot using EMG signals from human hand gestures** (2015, 3 citations), demonstrating how electromyography (EMG) signals can be used for intuitive, gesture-based robot control. This work highlights his interest in non-invasive interfaces that allow humans to command robots naturally. Kim’s research is particularly impactful for students and engineers working on autonomous systems, offering practical solutions for motion planning and human-robot synergy in dynamic environments.
Research Focus
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Top Papers
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