Yingpeng Wang
Papers
1
Total Citations
4
H-Index
1
About
Yingpeng Wang is a researcher focused on advancing robotic manufacturing, particularly in the domain of precision polishing and kinematic performance optimization. His work addresses critical challenges in industrial robotics, where achieving high-quality surface finishes requires sophisticated control over tool posture and motion constraints. Wang’s most notable contribution is the development of the KC-ADP (Kinematic Constraint-Aware Dynamic Programming) method, introduced in his 2025 paper “Posture optimization for improving the kinematics performance of robotic polishing under combined constraints.” This approach systematically optimizes robot arm configurations to enhance both accuracy and efficiency during polishing tasks, directly impacting industries such as aerospace and automotive manufacturing. Although early in his citation trajectory—with 4 citations on his top-cited work—Wang’s research demonstrates strong potential for influencing future robotic process optimization. His work bridges theoretical kinematics with practical application, offering a framework that balances multiple operational constraints. For students and researchers in robotics and manufacturing, Wang’s contributions provide a clear pathway for integrating optimization algorithms into real-world industrial systems, making him a promising voice in the evolution of intelligent robotic manufacturing.
Research Focus
Key Achievements
Top Papers
- 1