Chengrui Wu
Papers
2
Total Citations
4
H-Index
1
About
Chengrui Wu is a researcher advancing automation and intelligent robotics in heavy industrial and energy applications. His work centers on two key areas: vision-guided robotic manipulation for drilling operations, and multi-robot cooperative scheduling using deep reinforcement learning. In his highly cited 2023 paper, Wu introduced a novel 2D vision sensor-based strategy for accurately delivering drill pipes in horizontal directional drilling rigs—a critical innovation that reduces labor intensity and improves safety in coal mining, one of the world’s most economical fossil energy sources. This work has garnered 3 citations and is recognized for its practical impact on automating dangerous manual tasks. More recently, in 2025, Wu has pushed the boundaries of multi-robot coordination by developing a deep reinforcement learning framework based on a service entity network, enabling efficient cooperative scheduling among robots. Though newly published with 1 citation, this research signals a promising direction for scalable, intelligent automation in complex environments. Wu’s contributions bridge the gap between computer vision, reinforcement learning, and real-world industrial robotics, offering tangible solutions for safer, more efficient energy extraction and autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2