Guangfu Guo

Papers

Total Citations

H-Index

About

Guangfu Guo is a researcher whose work bridges robotics, computer vision, and intelligent control systems. His key research areas include robot arm kinematics, trajectory planning, and target recognition for humanoid robots. Guo’s major contribution lies in developing integrated approaches for robotic grasping, where he combines visual target identification with optimal motion planning. His most cited paper, “Trajectory planning of NAO robot arm based on target recognition” (2017), demonstrates a practical method for enabling NAO robots to autonomously locate and grasp objects. In this work, Guo establishes the robot arm’s kinematics using the Denavit-Hartenberg (D-H) modeling method and derives precise D-H kinematic parameters to compute optimal arm trajectories. This approach allows the robot to recognize target positions via NaoMarks and plan collision-free, efficient paths for object manipulation. While his citation count is modest, Guo’s work contributes to the foundational challenges of integrating perception and action in humanoid robotics—a critical step toward more autonomous and dexterous robotic systems. His research is particularly relevant for students and engineers working on robot control, computer vision, and human-robot interaction.