Papers
1
Total Citations
3
H-Index
1
About
Yao Duan is a rising researcher in autonomous robotics, whose work centers on intelligent path planning and 3D environment exploration using depth sensors. Duan’s key contribution is the development of THP (Tensor-field-driven Hierarchical Path Planning), a novel framework that leverages tensor fields to encode depth information for more efficient and autonomous scene exploration. This approach addresses a critical challenge in robotics—navigating unknown environments with limited sensor fields of view—by enabling robots to make smarter, hierarchical decisions in real time. Although early in their career, Duan’s flagship paper, published in 2024, has already garnered 3 citations, signaling growing interest from the robotics and computer vision communities. The work stands out for its elegant fusion of tensor mathematics with practical robotic navigation, offering a scalable solution for applications ranging from search-and-rescue to industrial inspection. Duan’s research promises to push the boundaries of how robots perceive and interact with complex, unstructured spaces, making them a name to watch in the field of autonomous systems.
Research Focus
Key Achievements
Top Papers
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