Minghan Xu

Papers

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About

Minghan Xu is a robotics researcher whose work focuses on advancing kinematic modeling and intelligent control for cooperative robotic systems. His primary research areas include inverse kinematics, neural network-based motion planning, and the optimization of multi-degree-of-freedom manipulators. In his most-cited paper, "Inverse Kinematics Analysis of 5-DOF Cooperative Robot Based on Long Short-Term Memory Network" (2023), Xu addresses critical challenges in robotic path planning—namely, lengthy computation times and inefficient search algorithms. By integrating LSTM networks with forward and inverse analytical kinematics models, he proposes a novel framework that significantly improves the speed and accuracy of motion solutions for 5-DOF cooperative robots. This work bridges traditional robotics theory with modern deep learning techniques, offering a practical pathway toward more responsive and adaptive automation. Although early in his career, Xu’s contributions are already gaining attention, with his research laying the groundwork for smarter, more efficient robotic systems in manufacturing and collaborative environments. His approach exemplifies how data-driven methods can enhance classical robotics, making him a promising voice in the field of intelligent automation.