Guanglei Li

About

Guanglei Li is a researcher advancing the frontiers of intelligent robotic manufacturing through innovative sensor fusion and optimization algorithms. His primary research areas include robot vision, point cloud processing, and inverse kinematics for industrial manipulators. Li’s most significant contribution is a novel automatic point cloud registration method that integrates an optimized RANSAC algorithm with an Improved Whale Optimization Algorithm (IWOA). This work, which has garnered 15 citations, directly addresses the critical challenges of low accuracy and efficiency in stereo camera systems for robotic manufacturing, enabling more precise 3D perception. He has also developed a hybrid ELM-SSA-SCA algorithm to solve 6-DOF robot inverse kinematics, enhancing both the speed and accuracy of motion planning. Additionally, Li proposed a Multidimensional Improved Eigenvalue Method (MIEM) for robustly identifying abnormal points in robot vision grinding systems, ensuring higher quality in automated surface finishing. Through these contributions, Li is systematically improving the reliability and precision of vision-guided robotic systems, making him a notable figure in the field of intelligent manufacturing and robotic automation.

Research Focus

Key Achievements

Top Papers

1. 1
   Automatic Registration Algorithm for the Point Clouds Based on the Optimized RANSAC and IWOA Algorithms for Robotic Manufacturing

   15 citations · 2022
2. 2
   Research on 6-DOF robot inverse kinematics based on blended optimization algorithm of ELM- SSA-SCA

   4 citations · 2022
3. 3
   Optimization of Abnormal Point Cloud Recognition in Robot Vision Grinding System Based on Multidimensional Improved Eigenvalue Method (MIEM)

   2 citations · 2020

Key Collaborators

Contact & Links