Papers
2
Total Citations
6
H-Index
2
About
Xufeng Zhu is a robotics researcher specializing in inverse kinematics, redundant manipulators, and deep learning applications for autonomous systems. His most-cited work, "An Analytical Solution to Inverse Kinematics of Seven Degree-of-freedom Redundant Manipulator" (2020, 4 citations), introduces a novel analytic method for computing inverse kinematics in S-R-S configuration manipulators. By decoupling redundancy through virtual arm angle parameters and leveraging traditional D-H modeling, Zhu's approach enables precise, real-time control of seven-degree-of-freedom robotic arms—a critical advancement for industrial automation and surgical robotics. His second notable contribution, "Study on Technologies of Overhead Line Recognition and Obstacle Distance Measurement by Patrol Robots Based on Deep Learning" (2020, 2 citations), applies neural networks and feature extraction to enhance autonomous power line inspection. This work bridges computer vision and robotics, enabling patrol robots to recognize overhead lines and measure obstacle distances with improved accuracy. Zhu's research demonstrates a dual focus on theoretical kinematics and practical deep learning integration, offering solutions that reduce computational complexity while boosting real-world reliability. His work is particularly valuable for researchers in robotic manipulation, autonomous navigation, and industrial inspection systems.
Research Focus
Key Achievements
Top Papers
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