Papers
2
Total Citations
8
H-Index
2
About
Mingfei Wan is a robotics researcher whose work focuses on advancing the state estimation and control of quadruped robots operating in challenging, non-stationary environments. His primary research areas include legged locomotion, sensor fusion, and disturbance rejection for autonomous systems. Wan’s major contribution is the development of an Invariant Extended Kalman Filter (IEKF) combined with a Disturbance Observer, which enables quadruped robots to accurately estimate their state—such as position, velocity, and orientation—even on uneven or moving terrain. This approach enhances robot stability and reliability in real-world applications like search-and-rescue, environmental monitoring, and precision agriculture. His most-cited paper, published in 2024, has already garnered 4 citations, signaling growing interest in his methods. Wan’s work is notable for bridging theoretical estimation algorithms with practical robotic deployment, offering a robust solution to the long-standing challenge of maintaining balance and accuracy in dynamic, unpredictable settings. His research is particularly valuable for students and engineers seeking to improve autonomous navigation in field robotics.
Research Focus
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Top Papers
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