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About

Aijun Yin is a leading researcher in intelligent robotics and autonomous systems, with a focus on vision-guided assembly, active vibration control, and obstacle recognition. His most cited work, “A Coarse-to-Fine Method for Estimating the Axis Pose Based on 3D Point Clouds in Robotic Cylindrical Shaft-in-Hole Assembly” (2021, 23 citations), introduces a novel 3D vision-based pose estimation method coupled with admittance control, significantly improving the efficiency of precision assembly tasks over traditional force-sensing approaches. In “Active Vibration Control of PID Based on Receptance Method” (2020, 8 citations), Yin advances robotic reliability by enabling arbitrary assignment of closed-loop poles and zeros in linear vibratory systems. His recent work on YOLO with feature enhancement (2024, 5 citations) applies deep learning to intelligent assembly, while his research on variational auto-encoders with reverse supervision (2020, 4 citations) enhances obstacle recognition for unmanned ground vehicles. Yin’s contributions bridge 3D computer vision, control theory, and deep learning, with applications spanning industrial automation and autonomous navigation. His work is widely cited for its practical impact on robotic manipulation and perception.