Fengyu Quan
Papers
6
Total Citations
37
H-Index
3
About
Fengyu Quan is a robotics researcher whose work spans autonomous perception, aerial manipulation, and neural scene reconstruction. His most influential contribution to date — "Active Implicit Object Reconstruction Using Uncertainty-Guided Next-Best-View Optimization" (2023, 19 citations) — advances the frontier of robot autonomy by integrating implicit neural representations with intelligent sensor-view planning, enabling mobile robots to reconstruct objects both accurately and efficiently. This work reflects his broader expertise in 3D perception and neural rendering, further demonstrated by his recent MSI-NeRF research linking panoramic depth estimation with generalizable neural radiance fields for immersive VR and robot sensing applications. Quan has also made notable contributions to aerial manipulation, developing singularity-robust hybrid visual servoing controllers (2018, 9 citations) and proxy-based super twisting algorithms to address the complex dynamic coupling inherent in multi-rotor robotic arm systems. His creative bio-inspired work — a swan-inspired UAV featuring a flexible long-neck perceptual system — highlights his inventive approach to robot design. Complementing his theoretical contributions, he developed a modular simulation platform for aerial manipulators operating in dynamic environments, lowering the barrier for safe algorithm development. Across these diverse threads, Quan consistently bridges perception, control, and autonomous decision-making in modern robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Singularity-Robust Hybrid Visual Servoing Control for Aerial Manipulator9 citations · 2018
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- 6Proxy-based Super Twisting Control Algorithm for Aerial Manipulators2 citations · 2023