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About

Xiang Yue is a robotics researcher whose work focuses on intelligent perception and autonomous navigation for specialized robotic systems, particularly in challenging environments such as power transmission lines and orchards. His key contributions lie in developing multisensor fusion and active vision strategies to enhance robot stability and obstacle-crossing capabilities. Notably, his most-cited paper, "Automatic Obstacle-Crossing Planning for a Transmission Line Inspection Robot Based on Multisensor Fusion" (17 citations), addresses the critical challenge of autonomous obstacle detection and behavior planning for line inspection robots. He further advanced this field by designing a centroid adjustment mechanism to improve robot stability when crossing jumper lines on live 110kV power lines. Yue has also drawn inspiration from biological systems, introducing chameleon-like active vision for wheeled mobile robots to improve environment perception. More recently, he has applied enhanced deep learning models, such as YOLOv5 with attention mechanisms, to agricultural robotics for apple harvesting in occluded environments. His work on vibration suppression for space robots demonstrates a broader interest in robotic dynamics. With a growing portfolio spanning inspection, agricultural, and space robotics, Yue is establishing himself as a versatile researcher in intelligent robotic systems.