Qinyu Cai
Papers
2
Total Citations
19
H-Index
2
About
Qinyu Cai is a researcher specializing in robotics, computer vision, and intelligent control systems, with a focus on enhancing robotic manipulation in complex, real-world environments. Their major contributions lie in integrating deep learning with probabilistic filtering techniques to improve robotic arm grasping and tracking accuracy. In their most-cited work, "A Hybrid YOLOv4 and Particle Filter Based Robotic Arm Grabbing System in Nonlinear and Non-Gaussian Environment" (2021, 17 citations), Cai proposed a novel framework combining YOLOv4 object detection with particle filter tracking, enabling robust performance under challenging nonlinear and non-Gaussian conditions. This approach significantly enhances a robotic arm’s ability to both detect and continuously track target objects, leading to more reliable grasping. Additionally, in "Deep Learning Based Strategy for Eye-to-Hand Robotic Tracking and Grabbing" (2020), Cai explored vision-guided robotic control using deep neural networks, further advancing autonomous manipulation. Through these works, Cai demonstrates a commitment to bridging perception and action in robotics, offering practical solutions for industrial automation and assistive technologies. Their research continues to inspire new directions in intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
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- 2