Xiaopei He

Papers

Total Citations

H-Index

About

Xiaopei He is a researcher whose work centers on intelligent mobile robotics, with a particular focus on local path planning and navigation in complex environments. He is best known for developing the Double BP Q-Learning algorithm, a novel fusion of backpropagation neural networks with reinforcement learning that addresses critical challenges in robotic motion. His most-cited paper, "Double BP Q-Learning Algorithm for Local Path Planning of Mobile Robot" (2021), tackles the "curse of dimensionality," poor model generalization, and the deadlock problem in obstacle-dense settings—common pitfalls in traditional Q-learning approaches. By integrating two BP neural networks into the Q-learning framework, He’s method enhances the robot’s ability to learn efficient, collision-free paths with reduced state-space complexity. This work has garnered 9 citations, reflecting its relevance to researchers in autonomous navigation and machine learning. He’s contributions are particularly valuable for advancing real-time decision-making in mobile robots, offering a more robust solution for dynamic environments. His research bridges the gap between theoretical reinforcement learning and practical robotic applications, making him a notable figure in the field of intelligent control systems.