Ouwen Zhu
Papers
1
Total Citations
4
H-Index
1
About
Ouwen Zhu is a researcher at the forefront of intelligent robotics and autonomous systems, with a focused expertise in reinforcement learning and motion control for Automated Guided Vehicles (AGVs). Their most cited work, "The Determination of Reward Function in AGV Motion Control Based on DQN" (2022, 4 citations), addresses a critical challenge in robotics: designing effective reward functions to stabilize AGV movement using Deep Q-Networks. This contribution bridges the gap between theoretical reinforcement learning algorithms and practical, real-world navigation, offering a systematic approach to improving motion stability and control efficiency. Zhu’s research is particularly valuable for advancing warehouse automation, smart manufacturing, and logistics, where reliable AGV performance is essential. By tackling the nuanced problem of reward shaping in dynamic environments, Zhu has laid groundwork for more adaptive and robust robotic systems. Their work demonstrates a clear commitment to translating complex AI models into tangible engineering solutions, making them a promising voice in the growing field of intelligent motion control.
Research Focus
Key Achievements
Top Papers
- 1The Determination of Reward Function in AGV Motion Control Based on DQN4 citations · 2022