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About

Guangzhu Chen is a robotics and autonomous systems researcher whose work centers on mobile robot navigation, path planning, and hydraulic robotics. His most influential contribution, "Path Planning Optimization of Indoor Mobile Robot Based on Adaptive Ant Colony Algorithm" (2021), has garnered an impressive 469 citations, establishing him as a significant voice in intelligent navigation research. In this work, Chen advanced the application of bio-inspired optimization techniques to solve complex indoor navigation challenges, improving both efficiency and adaptability in dynamic environments. Building on this foundation, Chen has pushed into cutting-edge machine learning territory, exploring immune deep reinforcement learning for robot path planning in unknown environments, reflecting his commitment to developing robust, generalizable autonomous systems. His research also extends into hydraulic robotics, where early investigations into electro-hydraulic joint design and trajectory optimization demonstrate a strong grounding in mechanical and control systems engineering. More recently, Chen has tackled industrial challenges through semantic SLAM methodologies tailored for production workshop environments, bridging perception, localization, and mapping under real-world uncertainty. Together, his body of work spans theoretical algorithm development and practical robotic engineering, making him a versatile contributor to the field of intelligent robotics and automation.