Papers
16
Total Citations
388
H-Index
9
About
Zhongtao Fu is a prominent robotics researcher whose work spans robot kinematics, coordinate calibration, dynamic parameter identification, and visual servoing. His research addresses some of the most mathematically demanding challenges in robotic systems, leveraging elegant theoretical frameworks — including geometric algebra, Lie theory, and dual quaternions — to solve real-world engineering problems with greater efficiency and generalizability. Fu's most cited contribution, "A Dual Quaternion-Based Approach for Coordinate Calibration of Dual Robots in Collaborative Motion" (2020, 94 citations), tackles the complex AXB=YCZ calibration problem essential for synchronized dual-robot systems. His 2013 work on inverse kinematics for 6R manipulators (63 citations) demonstrated an elegant geometric algebra solution to a notoriously difficult problem, while his Lie-theory-based dynamic parameter identification methodology (2020, 50 citations) offered a universal, streamlined alternative to cumbersome conventional approaches. Beyond theoretical contributions, Fu has made practical strides in robotic machining, collaborative welding trajectory generation, deep learning-based torque prediction using LSTM networks, and uncalibrated visual servoing. His body of work reflects a researcher equally at home in abstract mathematics and applied robotics, making sustained contributions that continue to shape how modern robotic systems are modeled, calibrated, and controlled.
Research Focus
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Top Papers
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- 7Joint torque prediction of industrial robots based on PSO-LSTM deep learning19 citations · 2024
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