Yun Ouyang
Papers
1
Total Citations
2
H-Index
1
About
Yun Ouyang is a rising researcher in the field of robotics, with a primary focus on motion planning and control for robotic manipulators. Their key contributions center on developing novel algorithms that integrate path optimization with inverse kinematics to solve complex, real-world manipulation challenges. Ouyang’s most-cited work introduces a groundbreaking method that combines Recursive Segmentation Point Migration Optimization (RSPMO) with Progressive Inverse Kinematics (PIK). This approach intelligently refines trajectories by detecting collisions on a direct path and dynamically adjusting segmentation points, enabling robotic arms to navigate cluttered environments with greater efficiency and safety. Though early in their career, with their top paper already garnering 2 citations, Ouyang’s work demonstrates a clear and promising impact on the field of obstacle-avoidance path planning. Their research is particularly notable for its practical, algorithmic approach to a persistent problem in industrial and service robotics, offering a scalable solution that balances computational efficiency with robust collision avoidance. As Ouyang continues to publish, their contributions are poised to influence both autonomous systems design and the broader advancement of intelligent robotic manipulation.
Research Focus
Key Achievements
Top Papers
- 1