Papers
3
Total Citations
13
H-Index
3
About
Kar Mun Chin is a robotics researcher whose work centers on advancing automated assembly and human-robot interaction. Her key research areas include computer vision for robotic manipulation, force-torque control strategies, and rehabilitation robotics. Her most cited paper, "Research on YOLOv8 Application in Bolt and Nut Detection for Robotic Arm Vision" (2024, 7 citations), addresses a critical gap in automated assembly by applying state-of-the-art deep learning to detect small, precision components—a foundational step for replacing human labor in industrial tasks. She further explores the challenge of precise nut-to-bolt mating in her review of advanced force-torque control strategies (2024, 3 citations), synthesizing passive and active compliance methods. Earlier, Chin contributed to rehabilitation robotics with a study on controller design for a two-link robotic orthosis (2019, 3 citations), using sinusoidal-input describing function models to improve trajectory tracking. Her work bridges practical industrial automation and assistive technology, demonstrating impact through targeted solutions for high-precision tasks.
Research Focus
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