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About

Han’s research focuses on mobile robotics, adaptive filtering, and intelligent control systems, with significant contributions to state estimation and fault-tolerant control for autonomous robots. His most cited work, “An Adaptive UKF Algorithm for the State and Parameter Estimations of a Mobile Robot” (2008, 25 citations), introduces a novel adaptive unscented Kalman filter that uses innovation-based cost functions and MIT rule to dynamically update process noise covariance, improving estimation accuracy and convergence speed—validated through simulations on omnidirectional robots. This work addresses critical limitations in prior knowledge of process noise distributions, enhancing UKF performance for active state and parameter estimation. Han also pioneered cognitive emotion models for eldercare robots in smart homes (2015, 4 citations), integrating Gabor filters, LBP, and KNN for facial expression recognition to enable empathetic human-robot interaction. His early work on online model and actuator fault-tolerant control (2007, 4 citations) introduced actuator effectiveness factors and square-root UKF for real-time health monitoring and reconfigurable control of mobile robots, demonstrated experimentally on 3-DOF omnidirectional platforms. Additional research spans voice-command intelligent control and inter-limb/intra-limb coordination for quadruped robots. With a total of 38 citations across his top papers, Han’s work bridges theoretical advances in adaptive filtering with practical applications in assistive robotics and autonomous navigation.