Hanwen Song
Papers
9
Total Citations
165
H-Index
7
About
Hanwen Song is a robotics researcher whose work centers on geometric calibration, kinematic modeling, and spatial transformation mathematics for industrial robotic systems. His primary contributions lie in developing novel methodologies for hand-eye calibration and robot-world calibration — foundational problems that determine how robots perceive and interact with their physical environment with precision. Song's most impactful work, garnering 38 citations, introduced a motion-capture-based approach to kinematic parameter identification, demonstrating a practical bridge between optical sensing technologies and robot calibration pipelines. Complementing this, his research on simultaneous robot-world and hand-eye calibration (34 citations) addressed a longstanding challenge by solving both transformation problems concurrently rather than sequentially, improving efficiency and accuracy. His exploration of dual quaternion algebra and motion tensors as mathematical frameworks for representing rigid body motion reflects a sophisticated theoretical approach, with multiple papers advancing both classic and entirely new solution paradigms. Notably, Song has pursued both closed-form algebraic solutions — including dual Kronecker product formulations and Lie group methods via Ad(SE(3)) — and data-driven approaches using artificial neural networks such as ELM. With over 165 cumulative citations across nine publications spanning just three years, Song has rapidly established himself as a productive and influential voice in the robotics calibration community.
Research Focus
Key Achievements
Top Papers
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- 8One-Step Solving the Hand–Eye Calibration by Dual Kronecker Product7 citations · 2024
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