YuAo Li

Wuhan University of Science and Technology

Papers

Total Citations

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About

YuAo Li is a pioneering researcher at the intersection of robotics and olfactory sensing, specializing in gas source localization using legged robotic platforms. His major contributions center on developing intelligent, learning-based approaches for odor source localization, moving beyond traditional rule-based algorithms that are limited to wheeled robots in simple terrains. Li’s most cited work, “Gas source localization using Dueling Deep Q-Network with an olfactory quadruped robot” (2024, 7 citations), introduces a novel reinforcement learning framework that enables quadruped robots to navigate complex, obstacle-laden environments while tracking chemical plumes. His earlier foundational study, “Multi-sensory Olfactory Quadruped Robot for Odor Source Localization” (2023, 6 citations), addresses the critical need for efficient and safe detection of hazardous chemical gas leaks, which pose severe threats to human safety and the environment. By integrating multiple sensory modalities with quadrupedal locomotion, Li’s work has significant implications for industrial safety, disaster response, and environmental monitoring. His research stands out for its practical focus on real-world deployment, demonstrating how advanced robotics and deep learning can solve pressing challenges in hazardous material detection.