Guangxin Li

China University of Petroleum, East China, Shandong University

Papers

Total Citations

191

H-Index

About

Guangxin Li is a robotics and artificial intelligence researcher whose work bridges intelligent optimization algorithms and machine vision systems for autonomous robotic applications. His research centers on two interconnected domains: swarm intelligence-based path planning and vision-guided robotic manipulation, areas of growing importance as robots are increasingly deployed in complex, dynamic environments. Li's most impactful contribution is his development of a hybrid optimization framework combining Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) algorithms for mobile robot path planning, which has garnered an impressive 108 citations since its 2023 publication — a remarkable reception for such a recent work, signaling its timeliness and practical relevance to the robotics community. His research in robotic grasping has been equally influential: his pixel-level grasp detection method, published in 2021 with 79 citations, introduces an adaptive, grasp-aware neural network architecture that moves beyond conventional discrete gripper configuration prediction, enabling more accurate and flexible object manipulation under real-world uncertainty in shape, pose, and size. Together, Li's contributions reflect a cohesive research vision — equipping robots with smarter decision-making capabilities, whether navigating environments or interacting with objects — making his work essential reading for students exploring autonomous robotics and computational intelligence.