Ruohuai Sun
Papers
9
Total Citations
80
H-Index
6
About
Ruohuai Sun is a leading researcher in intelligent robotics, with a primary focus on robotic manipulation, computer vision, and autonomous navigation. Sun’s most significant contribution is the development of the parallel YOLO-GG deep learning network for collaborative robots, which dramatically enhances the efficiency and precision of visual object recognition and grasping—a foundational work that has garnered 25 citations. To address the challenge of complex trajectory planning, Sun introduced the innovative M2ACD (Multi-Actor-Critic Deep Deterministic Policy Gradient) algorithm, enabling more efficient simulation-to-real transfer for robotic manipulators (16 citations). In the domain of semiconductor manufacturing, Sun advanced precision handling with an Active Wafer Centering algorithm, preventing costly contamination during wafer transfer. Further notable work includes a hybrid navigation algorithm for electric inspection robots and a CBAM-ASPP-SqueezeNet model for small-sample multi-target grasping. With a publication record spanning 2023 to 2025, Sun’s research is distinguished by its practical impact on industrial automation, from factory safety inspection to dynamic path planning, consistently pushing the boundaries of how robots perceive, plan, and act in complex environments.
Research Focus
Key Achievements
Top Papers
- 1Object Recognition and Grasping for Collaborative Robots Based on Vision25 citations · 2023
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- 8Research on Mobile Robot Navigation Method Based on Semantic Information4 citations · 2024
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