Shanen Yu
Papers
2
Total Citations
83
H-Index
1
About
Shanen Yu is a researcher advancing the frontiers of intelligent robotics and autonomous navigation. Their work centers on developing sophisticated path planning and trajectory optimization algorithms for robots operating in complex, unstructured environments. Yu’s most impactful contribution, the hybrid formation path planning algorithm published in 2022, masterfully combines A* search with a multi-target improved artificial potential field method. This approach has garnered 82 citations, reflecting its significance in enabling efficient, collision-free navigation for multi-robot systems in 2D random environments. More recently, Yu has extended their expertise to industrial applications, pioneering multiobjective optimization-based trajectory planning for laser 3D scanner robots—a critical innovation for high-precision manufacturing and inspection. By balancing competing objectives such as scanning accuracy, energy efficiency, and motion smoothness, this work promises to enhance the productivity and quality of automated laser scanning processes. Yu’s research not only addresses fundamental challenges in mobile robotics but also bridges the gap between theoretical algorithms and practical deployment, making their work essential reading for students and engineers seeking robust, real-world solutions in autonomous systems and robotic manipulation.
Research Focus
Key Achievements
Top Papers
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- 2