Fujin Li

North China University of Science and Technology

Papers

Total Citations

H-Index

About

Fujin Li is a researcher specializing in intelligent robotics and bionic learning algorithms, with a focus on advancing autonomous control systems. His work primarily explores the intersection of reinforcement learning, neural networks, and robotic motion planning. Li’s most cited paper, “The balance control of two-wheeled robot based on bionic learning algorithm” (2014, 6 citations), introduces a novel approach that combines a growing cell structure (GCS) network with Q-learning to achieve stable balance control—a critical challenge in two-wheeled robotics. This work stands out for its integration of self-organizing neural mechanisms with reinforcement learning, offering a more adaptive solution than traditional methods. In his second notable paper, “Research on Q-ELM algorithm in robot path planning” (2016, 4 citations), Li addresses the limitations of BP neural networks—such as high dimensionality and slow training—by proposing a Q-learning algorithm based on extreme learning machines (Q-ELM). This innovation enhances learning speed and efficiency in mobile robot path planning. Though his citation counts are modest, Li’s contributions are significant for their pioneering fusion of bionic principles with machine learning, laying groundwork for more responsive and efficient robotic systems. His work is particularly valuable for researchers exploring lightweight, adaptive control solutions in autonomous robotics.