Lianqin Yu
Papers
1
Total Citations
20
H-Index
1
About
Lianqin Yu is a researcher at the forefront of intelligent manufacturing and robotics, with a focus on integrating deep reinforcement learning into industrial logistics. Their most-cited work, "Spatiotemporal path tracking via deep reinforcement learning of robot for manufacturing internal logistics" (2023), has garnered 20 citations, highlighting its significance in advancing autonomous navigation for material handling systems. This contribution addresses critical challenges in dynamic factory environments, enabling robots to optimize path planning in both space and time, thereby improving efficiency and reducing operational costs. Yu's research bridges the gap between theoretical reinforcement learning algorithms and practical industrial applications, offering scalable solutions for smart factories. Their work is particularly notable for its emphasis on real-time adaptability, which is essential for modern manufacturing systems facing fluctuating demands. By pioneering spatiotemporal tracking methods, Yu has laid a foundation for more resilient and intelligent logistics networks. This achievement underscores their role as a key innovator in the evolving landscape of Industry 4.0, where robotics and AI converge to transform production workflows.
Research Focus
Key Achievements
Top Papers
- 1