Papers
4
Total Citations
46
H-Index
4
About
Zuguo Chen is a researcher whose work lies at the intersection of autonomous navigation, path planning, and intelligent robotic systems. His primary contributions focus on enhancing the efficiency and accuracy of mobile robot and unmanned aerial vehicle (UAV) operations through advanced algorithmic development. Notably, Chen proposed the Adaptive Iterated Square-Root Cubature Kalman Filter (AISRCKF) for Simultaneous Localization and Mapping (SLAM), a significant advancement that improves state estimation in complex environments. His most cited work, "Robot Search Path Planning Method Based on Prioritized Deep Reinforcement Learning" (2022), has garnered 32 citations, reflecting its impact on modern AI-driven robotics. Chen has also innovated in practical applications, such as developing a delta omnidirectional wheeled table tennis automatic pickup robot using visual servoing, and improving UAV path planning by integrating RRT with A* algorithms. Through these contributions, Chen demonstrates a consistent focus on bridging theoretical algorithms with real-world robotic autonomy, making his research valuable for students and engineers advancing the fields of SLAM, reinforcement learning, and autonomous navigation.
Research Focus
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Top Papers
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- 4A RRT path planning algorithm based on A* for UAV4 citations · 2022