Guangliang Liu

Shandong Academy of Sciences, Shandong Institute of Automation, Qilu University of Technology

Papers

Total Citations

H-Index

About

Guangliang Liu is a robotics researcher whose work focuses on the critical intersection of autonomous systems and hazardous environment operations, particularly in nuclear settings. His research spans mobile robot control, radiation source detection, and pedestrian detection algorithms, with an emphasis on practical implementations using the Robot Operating System (ROS) framework. Liu's most cited work, "Design of robot system for radioactive source detection based on ROS" (2020, 5 citations), presents a mobile robot platform that integrates radiation detectors and mechanical grippers to locate and handle radioactive materials, significantly reducing human radiation exposure. His contributions to neural network PID control systems for nuclear environment robots (2020, 2 citations) address the challenge of maintaining stable robot motion in complex, hazardous conditions. In pedestrian safety, Liu improved the YOLOv3-Tiny algorithm (2022, 2 citations) by adding 52×52 feature maps to enhance detection accuracy for small or occluded pedestrians, a critical advancement for autonomous navigation in crowded environments. While his citation counts reflect an emerging career, Liu's work demonstrates a clear commitment to solving real-world safety challenges through practical robotic systems, making his research particularly valuable for students and engineers working on robotics for hazardous applications.