Quanxing Xu
Papers
1
Total Citations
10
H-Index
1
About
Quanxing Xu is a pioneering researcher in the field of autonomous robotics, with a primary focus on intelligent path planning and reinforcement learning for mobile robots operating in unknown environments. His most-cited work, "Mobile robot path planning based on multi-experience pool deep deterministic policy gradient in unknown environment" (2024), has already garnered 10 citations, signaling its early impact on the robotics community. In this study, Xu introduces a novel deep reinforcement learning framework that leverages a multi-experience pool to enhance the Deep Deterministic Policy Gradient (DDPG) algorithm, enabling robots to navigate complex, uncharted terrains with improved efficiency and adaptability. This contribution addresses a critical challenge in autonomous navigation—balancing exploration and exploitation in dynamic settings—and offers a scalable solution for real-world applications such as search-and-rescue and industrial automation. Xu’s work stands out for its integration of advanced machine learning techniques with practical robotic systems, bridging the gap between theoretical algorithms and deployable solutions. As a rising scholar, his research continues to inspire new directions in adaptive robotics, promising safer and more reliable autonomous systems for the future.
Research Focus
Key Achievements
Top Papers
- 1