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About

Xuequn Zhang is a leading researcher in robotics and human–robot interaction, with a focus on advancing the control and autonomy of robotic systems. Their work spans key areas including inverse kinematics, adaptive control, and mobile robot localization. Zhang’s most cited paper (56 citations) introduces a novel inverse kinematics algorithm for six-DOF robot manipulators using screw theory, significantly improving computational efficiency over traditional Denavit-Hartenberg methods. In human–robot interaction, Zhang developed a single-input adaptive fuzzy sliding mode controller for lower extremity exoskeletons (21 citations), enhancing tracking performance and reducing interaction forces by accurately interpreting wearer motion intent. For autonomous navigation, Zhang proposed a hybrid visual natural landmark–based localization method for indoor mobile robots (11 citations), leveraging ceiling and environmental features for robust position estimation. These contributions demonstrate Zhang’s impact in making robotic systems more efficient, responsive, and self-reliant, with applications ranging from industrial automation to assistive exoskeletons. Their work is widely cited by researchers in robotics, control engineering, and human–robot collaboration.