Zongmin Qiu
Papers
1
Total Citations
3
H-Index
1
About
Zongmin Qiu is a researcher at the forefront of applying artificial intelligence to industrial logistics and energy systems. His work primarily focuses on the intersection of deep reinforcement learning and automated warehouse management, with a particular emphasis on optimizing battery usage for robotic fleets. In his most cited paper, "Battery Management for Automated Warehouses via Deep Reinforcement Learning" (2020), Qiu introduced a novel framework that uses reinforcement learning to dynamically schedule battery charging and swapping for autonomous guided vehicles, significantly improving operational efficiency and reducing downtime in large-scale warehouse environments. This contribution addresses a critical bottleneck in modern logistics—energy management—and has garnered attention from both academia and industry, with 3 citations to date. Qiu’s research is notable for bridging theoretical advances in machine learning with practical, real-world applications, offering scalable solutions for the growing demands of e-commerce and automated supply chains. His work continues to influence the design of intelligent energy-aware systems in robotics and industrial automation.
Research Focus
Key Achievements
Top Papers
- 1