Xiaoshuang Xiong
Papers
1
Total Citations
20
H-Index
1
About
Xiaoshuang Xiong is a leading researcher in intelligent manufacturing and robotics, specializing in the integration of deep reinforcement learning for autonomous industrial logistics. Her most-cited work, "Spatiotemporal path tracking via deep reinforcement learning of robot for manufacturing internal logistics" (2023, 20 citations), introduces a novel framework that enables robots to dynamically navigate complex factory environments by learning optimal spatiotemporal paths in real time. This contribution addresses critical challenges in manufacturing efficiency, reducing human intervention while improving precision and adaptability in material handling. Xiong’s research bridges the gap between theoretical reinforcement learning algorithms and practical robotic applications, offering scalable solutions for smart factories. Her work has been recognized for its potential to transform internal logistics, a cornerstone of Industry 4.0, and has garnered attention from both academic and industrial sectors. With a focus on real-world impact, Xiong continues to advance autonomous systems that enhance productivity and safety in manufacturing settings.
Research Focus
Key Achievements
Top Papers
- 1