Xuanzhen Xu
Papers
4
Total Citations
50
H-Index
4
About
Xuanzhen Xu is a rising force in intelligent robotics, specializing in motion planning, multi-robot collaboration, and the integration of machine learning with control systems. His work addresses critical challenges in high-degree-of-freedom (DoF) manipulators and autonomous robot teams, blending theoretical rigor with practical deployment. Xu’s most cited paper (27 citations) introduces a k-means clustering-enhanced SVM for rapid pattern recognition during curve negotiation, enabling robust classification of flying and mobile robots in collaborative tasks. He further advances adaptive control with a self-adaptive robust motion planning framework using deep Model Predictive Control (MPC), achieving flexibility under uncertainty (11 citations). Notably, Xu pioneers the use of large language models (LLMs) for precision kinematic path optimization in high-DoF manipulators, bridging semantic reasoning with real-world robotic execution (8 citations). His contributions are already shaping next-generation autonomous systems, demonstrating how clustering, deep learning, and NLP can synergize to create more intelligent, responsive robots. With over 50 total citations in just 2024, Xu is establishing himself as a key innovator at the intersection of robotics and artificial intelligence.
Research Focus
Key Achievements
Top Papers
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