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About

Guojun Zhao is a leading researcher in robotics and intelligent control, with a focus on trajectory optimization, inverse kinematics, and autonomous navigation. His work addresses critical challenges in manufacturing and automation, particularly for mobile and manipulator robots. Zhao’s most influential paper, “Genetic Algorithm-Based Trajectory Optimization for Digital Twin Robots” (2022, 156 citations), introduces a novel method to enhance the accuracy and efficiency of mobile robot trajectories in material handling, directly impacting industrial productivity. He further advances robotic control with “A Tandem Robotic Arm Inverse Kinematic Solution Based on an Improved Particle Swarm Algorithm” (2022, 78 citations), offering a robust alternative to traditional analytical methods for solving complex kinematic problems. In “Multi-Objective Location and Mapping Based on Deep Learning and Visual SLAM” (2022, 32 citations), Zhao improves map readability and interactivity for intelligent robots operating in unknown environments. His latest work, “Improved Bald Eagle Search Optimization Algorithm for the Inverse Kinematics of Robotic Manipulators” (2024), continues to push boundaries in solving nonlinear, coupled kinematic challenges. With over 270 total citations, Zhao’s contributions are shaping the future of smart manufacturing and autonomous systems.