Terence Sy Horng Ting
Papers
2
Total Citations
10
H-Index
2
About
Terence Sy Horng Ting is a rising researcher in robotic automation, specializing in vision-based detection and precision control for assembly tasks. His work focuses on integrating computer vision and force-torque control to enable robots to perform complex, high-precision operations like bolt-and-nut mating. In his most-cited paper, "Research on YOLOv8 Application in Bolt and Nut Detection for Robotic Arm Vision" (2024, 7 citations), Ting demonstrates how deep learning object detection can be applied to identify small industrial components in a robot's workspace—a critical first step toward fully automated assembly. His second major work, "A Review of Advanced Force Torque Control Strategies for Precise Nut-to-Bolt Mating in Robotic Assembly" (2024, 3 citations), systematically evaluates passive compliance, active control, and manual teaching methods, providing a valuable roadmap for researchers tackling alignment challenges in high-precision robotics. Though early in his career, Ting’s contributions are already shaping the future of automated manufacturing, bridging the gap between visual perception and physical manipulation. His work is particularly relevant for students and engineers interested in Industry 4.0, offering practical insights into how robots can reliably handle tasks that require both sight and touch.
Research Focus
Key Achievements
Top Papers
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