Yebin Wang
Papers
8
Total Citations
143
H-Index
5
About
Yebin Wang is a researcher whose work spans robust state estimation, mobile robot motion planning, and networked control systems — areas at the intersection of control theory, machine learning, and robotics. His most influential contribution, "Robust Extended Kalman Filtering for Systems With Measurement Outliers" (2021, 68 citations), addresses a critical vulnerability in nonlinear state estimation when sensor data is corrupted by errors, environmental disturbances, or cyberattacks — a challenge of growing relevance in modern autonomous and industrial systems. Complementing this, his work on learning heuristic functions for mobile robot path planning using deep neural networks (2019, 30 citations) demonstrates his ability to bridge data-driven techniques with classical planning algorithms like A*, significantly improving computational efficiency. His research on co-designing safe and efficient networked control systems for factory automation (2019, 28 citations) reflects a strong interest in cyber-physical systems under realistic communication constraints. Further contributions to continuous curvature path planning, data-driven motion planning with safety guarantees, and supervised learning for robotic payload estimation underscore a career devoted to making autonomous systems smarter, safer, and more robust across diverse real-world applications.
Research Focus
Key Achievements
Top Papers
- 1Robust Extended Kalman Filtering for Systems With Measurement Outliers68 citations · 2021
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- 5Robust Extended Kalman Filtering for Systems with Measurement Outliers5 citations · 2019
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- 8On existence conditions of a class of continuous curvature paths2 citations · 2017