Papers
8
Total Citations
164
H-Index
7
About
Xiaoping Liu is a prolific robotics and automation researcher whose work spans robot calibration, mobile robot control, intelligent logistics, and modular robot systems. Over two decades of contributions, Liu has consistently advanced the intersection of intelligent algorithms and practical robotic applications. Among Liu's most impactful contributions is a dual quaternion algebra-based method for robot base frame calibration (2018, 42 citations), offering a precise solution for coordinate transformation critical to high-accuracy motion planning. Complementing this, Liu has made significant strides in mobile robot control, developing RBF neural network-based adaptive motion controllers to combat wheel slippage in challenging environments (2019, 27 citations) and robust iterative learning control algorithms for tracked mobile robots operating under disturbances (2022). Liu's research extends into intelligent logistics, with deep learning-driven visual sorting systems for express parcels garnering notable attention (2020, 31 citations). Earlier foundational work includes genetic algorithm-enhanced neural network controllers for vibration suppression in modular robots (2005, 25 citations) and dynamic modeling of innovative spherical robots with arms using Kane's Method (2008). Across a career bridging classical control theory and modern machine learning, Xiaoping Liu's cumulative citation record reflects sustained relevance and broad influence across robotics research communities worldwide.
Research Focus
Key Achievements
Top Papers
- 1A Method of Robot Base Frame Calibration by Using Dual Quaternion Algebra42 citations · 2018
- 2Visual Sorting of Express Parcels Based on Multi-Task Deep Learning31 citations · 2020
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- 5Dynamic Modeling of a Spherical Robot with Arms by Using Kane's Method13 citations · 2008
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- 8Visual Sorting Method Based on Multi-Modal Information Fusion4 citations · 2022