Guang Qiu

Chongqing University of Posts and Telecommunications

Papers

Total Citations

H-Index

About

Guang Qiu is a researcher focused on advancing industrial robotics through intelligent computational methods. His primary research areas include robotics kinematics, optimization algorithms, and neural network applications. Qiu’s most notable contribution is his work on solving the inverse kinematics problem for industrial robots, a critical challenge in automation where traditional solutions often suffer from slow computation and low precision. In his highly regarded 2020 paper, he introduced a novel hybrid approach that integrates Particle Swarm Optimization (PSO) with Radial Basis Function Neural Networks (RBFNN). This PSO-RBFNN algorithm significantly enhances both the speed and accuracy of kinematic solutions, offering a practical and efficient method for real-world robotic control. With 6 citations, this work has already garnered attention for its direct impact on improving robot performance in manufacturing and assembly tasks. Qiu’s research bridges the gap between theoretical optimization and applied robotics, making him a valuable contributor to the field of intelligent automation.