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About

Liang Guo is a leading researcher in intelligent robotics and autonomous navigation, with a particular focus on semantic perception and multi-robot coordination. His most impactful work, "YOLO-SLAM: A semantic SLAM system towards dynamic environment with geometric constraint" (2022, 255 citations), introduces a groundbreaking approach to simultaneous localization and mapping that integrates deep learning-based object detection with geometric constraints, enabling robots to robustly operate in dynamic, real-world environments. This work has become a key reference for researchers tackling the challenge of semantic SLAM. Guo has also made significant contributions to collision-free multi-robot navigation, developing a novel approach that combines ORCA with linear MPC to address nonholonomic constraints in wheeled mobile robots (2020, 23 citations). In the domain of industrial robotics, he designed a discontinuous fold line welding seam recognition and tracking system for narrow spaces in shipbuilding (2019), demonstrating practical applications of mobile robot technology. His work on obstacle detection for power transmission lines using deep learning (2019) further showcases his versatility in applying AI to real-world robotic challenges. Through these contributions, Liang Guo has established himself as a key innovator at the intersection of semantic perception, autonomous navigation, and industrial robotics.