Liang Zhao

Beijing University of Technology, Xi'an University of Architecture and Technology

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About

Liang Zhao is a robotics researcher whose work spans mobile robot navigation, human detection and tracking, and intelligent path planning in complex environments. With a focus on bridging perception and autonomy, Zhao has made meaningful contributions to the field of autonomous robotic systems over more than a decade of research. Among Zhao's most recognized contributions is a robust human detecting and tracking framework that leverages stereo vision combined with Extended Kalman Filtering (EKF), enabling mobile robots to reliably identify and follow humans across diverse motion postures — work that has garnered 10 citations since its 2012 publication. Complementing this, Zhao developed a 3D environment digitalization technique using scene flow and multi-view stereo reconstruction, advancing mobile robot navigation through nonrigid motion analysis. More recently, Zhao has turned attention to the challenging domain of underground mining robotics, publishing a 2024 study on an improved Rapidly Exploring Random Tree (RRT) algorithm for path planning in unstructured subterranean environments, which has already attracted 9 citations — reflecting strong contemporary relevance. Zhao's body of work demonstrates a consistent commitment to solving real-world robotic challenges, from human-robot interaction to industrial safety applications in hazardous mining settings.