Mingxing Fang
Papers
2
Total Citations
11
H-Index
2
About
Mingxing Fang is a rising researcher in robotics and autonomous systems, with key contributions in sensor calibration and control of robotic manipulators. His work addresses critical challenges in perception and actuation for next-generation autonomous platforms. Fang’s most notable contribution is the development of LiDAR-Link, an observability-aware, probabilistic plane-based method for extrinsic calibration of non-overlapping solid-state LiDARs. This work, published in 2024, directly tackles a fundamental bottleneck in deploying multiple solid-state LiDARs with limited or non-overlapping fields of view—a common configuration in modern autonomous driving and mobile robotics. With 7 citations, this paper has quickly gained recognition for enabling robust, accurate multi-sensor fusion. In parallel, Fang has advanced control theory for robotic arms, proposing an Equivalent Input Disturbance (EID) approach to suppress vibration and reject disturbances in flexible joints. This 2023 work, with 4 citations, improves the precision and reliability of robotic manipulation in the presence of internal and external perturbations. Together, Fang’s research bridges perception and control, offering practical solutions for more resilient and perceptive autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2