Jungmin Kang
Papers
1
Total Citations
7
H-Index
1
About
Jungmin Kang is a researcher whose work focuses on the intersection of robotics, control systems, and machine learning, with a particular emphasis on enhancing the precision and reliability of cable-driven parallel robots (CDPRs). Their most notable contribution, the 2019 paper "Position error prediction using hybrid recurrent neural network algorithm for improvement of pose accuracy of cable driven parallel robots," introduces a novel hybrid recurrent neural network approach to predict and mitigate position errors in these complex robotic systems. This work addresses a critical challenge in CDPRs—achieving high pose accuracy despite cable elasticity, friction, and dynamic uncertainties—by leveraging deep learning to model and correct errors in real time. With 7 citations, this paper has garnered attention from researchers in robotics and automation, reflecting its practical relevance for applications such as manufacturing, large-scale 3D printing, and rehabilitation devices. Kang’s research demonstrates a commitment to bridging theoretical algorithms with tangible improvements in robotic performance, making their work valuable for students and engineers seeking to advance precision control in non-conventional robotic architectures.
Research Focus
Key Achievements
Top Papers
- 1