Qianqian Fang
Papers
11
Total Citations
190
H-Index
8
About
Qianqian Fang is a leading researcher in robotics, specializing in robot dynamics, collision detection, and human-robot interaction. Their work focuses on developing advanced methods for identifying and calibrating robot physical parameters, enabling safer and more precise robotic systems. Fang's most-cited paper, "Dynamic Identification of the KUKA LBR iiwa Robot With Retrieval of Physical Parameters Using Global Optimization" (2020, 42 citations), introduces a novel approach to extracting fundamental dynamic parameters for computing link mass matrices. They have also made significant contributions to wall-climbing robot design, with their permanent-magnetic adsorption mechanism paper (2019, 38 citations) addressing critical limitations in adsorption capability. Fang's research on collision detection without torque sensors, including a modified nonlinear disturbance observer based on neural networks (2019, 15 citations) and a current residuals method for UR10 robots (2024, 16 citations), has advanced safe human-robot collaboration. Their work on robot dynamic calibration at the current level (2022, 27 citations) and synthetic inverse kinematic algorithms for 7-DOF redundant manipulators (2018, 15 citations) further demonstrates their impact on practical robotics applications.
Research Focus
Key Achievements
Top Papers
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- 5A Synthetic Inverse Kinematic Algorithm for 7-DOF Redundant Manipulator15 citations · 2018
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