Papers
13
Total Citations
75
H-Index
4
About
Jin-Hyun Park is a robotics and intelligent systems researcher whose work spans trajectory optimization, motion control, and applied machine learning for robotic applications. Over more than two decades, Park has made consistent contributions to the field of robot manipulator control, most notably through the application of evolutionary computation techniques to solve complex optimization problems. His landmark 2000 paper on optimal trajectory planning using evolution strategy and sliding mode control, which has garnered 21 citations, established a foundation for minimum-time trajectory planning under real-world kinematic and dynamic constraints — a persistent challenge in industrial robotics. Subsequent work extended these methods to incorporate fuzzy logic and cubic polynomial joint trajectories, reflecting a commitment to hybrid intelligent control architectures. Park's research has evolved with emerging technologies: his later work addresses mobile robot control using genetic algorithms and neural networks, predictive control under dynamic constraints, and robotic grasping using pneumatic sensing and machine learning. A 2023 contribution applying optimized YOLO-based deep learning networks to cigarette defect detection demonstrates his engagement with modern computer vision in manufacturing quality control. With cumulative citations across diverse robotic domains and research spanning foundational control theory to practical industrial AI, Park represents a versatile researcher bridging classical optimization with contemporary intelligent systems, offering valuable insights for students working at the intersection of robotics, control engineering, and machine learning.
Research Focus
Key Achievements
Top Papers
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- 7Stable Grasping of Objects Using Air Pressure Sensors on a Robot Hand4 citations · 2018
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- 10Development of robot manipulation technology in ROS environment3 citations · 2017