Papers
7
Total Citations
50
H-Index
5
About
Shuopeng Wang’s research lies at the intersection of robotics, computer vision, and intelligent control, with a particular focus on enhancing robotic perception and manipulation in complex, real-world environments. His most impactful work, a 2022 study on a weld feature points detection method using an improved YOLO algorithm for welding robots in high-noise conditions, has garnered 20 citations, demonstrating its significance for industrial automation. Wang has also made notable contributions to robot auditory systems, designing a Kinect-based auditory system for mobile robots and developing a position fingerprint localization method using linear interpolation to improve human-robot interaction. His research extends to vision-based object grasping for robotic manipulators, where he proposed a binocular vision control method for service robots, and to soft robotics, with a review on reinforcement learning controllers for soft manipulators. Additionally, Wang has explored lower extremity exoskeleton robots, conducting systematic experimental studies to address time-varying and nonlinear control challenges. His recent work on an improved YOLO-based laser stripe region extraction method for welding robots further underscores his commitment to advancing real-time, robust robotic vision systems.
Research Focus
Key Achievements
Top Papers
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- 4Vision Based Object Grasping of Robotic Manipulator6 citations · 2018
- 5Review on Reinforcement Learning Controller in Soft Manipulator5 citations · 2021
- 6Experimental Study of Lower Extremity Exoskeleton Robot4 citations · 2020
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