Papers
7
Total Citations
32
H-Index
4
About
Xiaodong Zhuang is a researcher whose work focuses on the intersection of mobile robotics, reinforcement learning, and fuzzy logic, with a particular emphasis on navigation and path planning in complex and dynamic environments. His major contributions include pioneering the integration of entropy into reinforcement learning for mobile robot control, where he defined local and global strategy entropy as a quantitative, problem-independent measure of learning progress. He also advanced the field by combining temporal difference learning with fuzzy state representation to transform state evaluation functions into discrete artificial potential fields, enabling globally optimal path planning. Additionally, Zhuang developed a hybrid intelligent control system that fuses fuzzy modelization with reinforcement learning to achieve robust, self-adaptive navigation. His work on fuzzy concept-based mathematical models for dynamic environments improved local searching efficiency by leveraging object movement information. With over 30 citations across his most-cited papers, Zhuang’s research has laid foundational groundwork for adaptive robot control. Notable achievements include his early application of cellular neural networks for real-time path planning and his exploration of extended spatial and temporal learning scales to enhance reinforcement learning efficiency.
Research Focus
Key Achievements
Top Papers
- 1Robust Mobile Robot Localization Using a Evolutionary Particle Filter10 citations · 2005
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- 7Reinforcement learning with extended spatial and temporal learning scale2 citations · 2004