Na Guo

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About

Na Guo is a leading researcher in mobile robotics, specializing in intelligent path planning and autonomous navigation in complex, unknown environments. Her major contributions address a critical bottleneck in robotics: enabling mobile robots to avoid obstacles and reach targets without getting stuck in "dead zones." She pioneered an enhanced Artificial Potential Field method that overcomes the traditional local minimum problem, a fundamental issue that often causes robots to fail. Expanding on this, Guo introduced a novel approach using Long Short-Term Memory (LSTM) neural networks, allowing robots to learn and adapt their path planning strategies dynamically rather than relying on static, environment-specific algorithms. Her two most-cited papers—on artificial potential fields (26 citations) and LSTM-based planning (24 citations)—are foundational works that have guided subsequent research in adaptive and robust robot navigation. By bridging classical control theory with modern deep learning, Guo’s work provides practical, scalable solutions for autonomous systems, making her a key figure in advancing intelligent mobile robotics.