Papers
29
Total Citations
958
H-Index
15
About
Huashan Liu is a prominent robotics researcher whose work spans trajectory planning, kinematic analysis, dynamic parameter identification, and intelligent control of robot manipulators. His research has made substantial contributions to both the theoretical foundations and practical implementation of robotic systems, earning him widespread recognition in the field. Liu's early work established strong fundamentals in robot motion science. His 2012 paper on time-optimal, jerk-continuous trajectory planning for manipulators with kinematic constraints has accumulated 257 citations, becoming a landmark reference for smooth and efficient robot motion generation. Complementing this, his closed-loop dynamic parameter identification method using modified Fourier series demonstrated rigorous system modeling capabilities. His efficient inverse kinematics algorithm for PUMA560-structured robots, with 118 citations, further showcased his ability to translate complex mathematical formulations into computationally practical solutions. In recent years, Liu has embraced deep reinforcement learning, proposing general motion planning frameworks and novel actor-critic architectures for redundant manipulators navigating obstacle-laden environments. His work on flexible-joint robot control — spanning adaptive neural backstepping, sliding mode chattering suppression, and singularly perturbed decoupling — reflects a deep commitment to robust real-world performance. Collectively, his research positions him as a versatile and impactful contributor to modern robotics engineering.
Research Focus
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Top Papers
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- 6Easy industrial robot cell coordinates calibration with touch panel44 citations · 2017
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