Qiuzhuo Liu

Merchants Chongqing Communications Research and Design Institute

Papers

Total Citations

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About

Qiuzhuo Liu’s research centers on intelligent infrastructure inspection, with a particular focus on robotic systems for tunnel maintenance and safety. Liu’s major contributions lie in developing advanced diagnostic and detection methods that integrate fuzzy logic and laser-based sensing technologies. In the 2020 paper “Research on Fault Diagnosis Method of Tunnel Inspection Robot Based on T−S Fuzzy FTA,” Liu proposed a T-S fuzzy fault tree analysis (FTA) model to enable intelligent fault diagnosis, using the robot’s positioning system as a case study. This work provides a systematic approach to identifying and mitigating failures in complex robotic systems. Complementing this, Liu’s “Road Tunnel Detection Robot and Method Based on Laser Point Cloud” addresses critical challenges in road tunnel maintenance—such as water seepage, surface cracking, and spalling—by introducing a laser point cloud-based detection method. These contributions are foundational to improving the reliability and safety of tunnel inspection robots, directly impacting infrastructure longevity and vehicle safety. With each paper garnering 3 citations, Liu’s work is a targeted, early-stage contribution to the growing field of automated infrastructure monitoring.