Hanxuan Guo
Papers
2
Total Citations
47
H-Index
2
About
Hanxuan Guo is a robotics researcher whose work focuses on intelligent control and kinematic optimization for robotic manipulators. His primary research areas include inverse kinematics, visual servoing, and machine learning-based control systems. Guo’s major contribution lies in developing computationally efficient algorithms that balance speed and accuracy in robotic motion planning. His most cited paper, “Inverse kinematics solution for robotic manipulator based on extreme learning machine and sequential mutation genetic algorithm” (2018, 36 citations), introduces a hybrid intelligent approach that significantly reduces computational time for six-degree-of-freedom manipulators. This work has been foundational for real-time robotic applications. More recently, Guo advanced uncalibrated visual servoing by integrating Kalman filtering with mixed-kernel online sequential extreme learning machines (2023, 11 citations), enabling robust robot control without precise calibration. His research demonstrates a clear trajectory from theoretical algorithm development to practical implementation in vision-guided robotics. Guo’s work is particularly valuable for students and researchers interested in bridging machine learning with traditional robotics challenges, offering solutions that improve both performance and efficiency in autonomous manipulation systems.
Research Focus
Key Achievements
Top Papers
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