Papers
6
Total Citations
36
H-Index
3
About
Chen Yang’s research lies at the intersection of robotics, autonomous navigation, and bionic vision, with a particular focus on enabling intelligent motion and perception for unmanned and mobile systems. His most influential work, a 2011 paper on three-dimensional path planning for UAVs using linear programming (19 citations), introduced a rigorous optimization-based approach for pursuing targets while avoiding static and dynamic obstacles—a foundational contribution to aerial robotics. He further advanced mobile robot navigation with the “relative state tree” method (2014) and path planning in relative velocity coordinates (2011), offering novel frameworks for safe, efficient motion in complex environments. In perception, Yang developed a binocular vision-based automatic aiming method for mobile robots (2021) and a deep learning approach for dynamic target detection and tracking in water (2020), bridging computer vision and real-world robotic tasks. His most recent and notable work (2025) presents a micro biomimetic eyeball for humanoid robots, integrating optical imaging and dynamic field-of-view modulation via an origami mechanism—a high-impact innovation for general-purpose humanoid vision. With a career spanning foundational path planning to cutting-edge bio-inspired hardware, Chen Yang’s contributions continue to shape autonomous robotics and intelligent perception systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Knowledge-driven path planning for mobile robots: relative state tree7 citations · 2014
- 3Mobile Robot Automatic Aiming Method Based on Binocular Vision4 citations · 2021
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- 5
- 6Path Planning of Mobile Robot in Relative Velocity Coordinates2 citations · 2011