Hongfei Zu
Papers
5
Total Citations
124
H-Index
4
About
Hongfei Zu is a robotics researcher specializing in kinematic calibration, positioning accuracy, and error compensation for industrial and heavy-load robotic systems. His work addresses one of the most critical challenges in advanced robotic manufacturing: achieving precise and reliable robot positioning under real-world operating conditions. Zu's most influential contribution, cited 52 times, introduces a hybrid calibration framework that combines model-based geometric parameter identification using the product of exponentials (POE) formula with an optimized neural network to compensate for nongeometric errors — a powerful approach that bridges analytical rigor with data-driven flexibility. His 2019 algorithm for robust kinematic calibration, which has garnered 40 citations, further established him as a leading voice in manipulator reliability research. Complementing these efforts, he has explored laser tracking-based accuracy improvement, RBF neural network modeling of joint friction using beetle antennae search optimization, and enhanced POE-based methods for identifying transmission errors in serial robots. Across his body of work, Zu consistently integrates classical robotics theory with intelligent computational techniques, making his research both practically applicable and technically innovative. With over 120 cumulative citations, his contributions offer valuable tools for engineers and researchers striving to elevate precision in next-generation robotic manufacturing systems.
Research Focus
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Top Papers
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