Yun Tao Song

Institute of Plasma Physics, China Jiliang University

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About

Yun Tao Song is a leading researcher in robotic systems for extreme environments, with a primary focus on fusion energy infrastructure and industrial safety inspection. His most influential work centers on the development of articulated robotic arms for tokamak fusion devices, most notably the Articulated Inspection Arm (AIA) for fusion purposes (2015, 26 citations), which addresses the critical challenge of in-vessel inspection within high-radiation, vacuum conditions. Song’s foundational design feasibility analysis for the EAST tokamak (2011, 8 citations) established key engineering principles for robots operating inside superconducting fusion reactors, enabling safer and more reliable maintenance of plasma-facing components. More recently, he has expanded into autonomous quadruped robots for urban natural gas pipe leakage inspection (2024), proposing a novel hybrid ORRT*-DWA path planning algorithm that enhances navigation efficiency in complex pipeline networks. With a career spanning over a decade, Song’s work bridges the gap between advanced robotics and critical energy infrastructure, contributing directly to the operational safety and longevity of fusion experiments and public utility systems. His research continues to influence the design of resilient, task-specific robots for hazardous, confined, and remote environments.