Shuli Chen
Papers
2
Total Citations
49
H-Index
2
About
Shuli Chen is a leading researcher in mobile robotics and intelligent control systems, with a focus on enabling autonomous navigation in complex, dynamic environments. Her work centers on developing novel path planning and behavioral decision-making algorithms that allow robots to adapt to real-world uncertainties. Chen’s most influential contribution is the integration of fuzzy artificial potential fields with extensible neural networks for mobile robot path planning—a method that significantly improves obstacle avoidance and trajectory optimization in unpredictable settings. This work has garnered 36 citations, underscoring its impact on the field. She has also advanced reinforcement learning (RL) for robotics by introducing a neurophysiologically motivated model that addresses the critical exploration-exploitation trade-off in online, model-free RL. This approach, detailed in her 2020 paper with 13 citations, offers a biologically inspired solution for real-time behavioral decision-making, enhancing robot autonomy without requiring pre-trained models. Chen’s research bridges computational intelligence and robotics, providing practical frameworks for safer, more efficient autonomous systems. Her achievements highlight a dedication to solving core challenges in mobile robotics, making her work essential reading for students and researchers in autonomous navigation and intelligent control.
Research Focus
Key Achievements
Top Papers
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