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About

Yaduo Pan is a pioneering researcher at the intersection of robotics, artificial intelligence, and digital twin technology. His work primarily focuses on two transformative areas: olfactory robotics for hazardous gas detection and high-fidelity digital twin modeling for industrial automation. Pan’s most significant contributions include developing a Dueling Deep Q-Network (DDQN) approach for gas source localization using quadruped robots—a breakthrough that moves beyond traditional rule-based algorithms for wheeled robots, enabling effective operation in complex, obstacle-filled terrains. His multi-sensory olfactory quadruped robot research addresses the critical challenge of safely locating toxic and explosive chemical leaks, with his 2024 paper on this topic already accumulating 7 citations. In parallel, Pan has advanced digital twin technology for industrial robots, proposing a multi-level, multi-domain modeling method that achieves high fidelity—essential for accurate simulation and predictive maintenance. His 2025 paper on this subject has rapidly gained 11 citations, reflecting its immediate impact. Through his work, Pan is shaping the future of both hazardous environment robotics and smart manufacturing, bridging the gap between virtual models and physical robotic systems.