Xiangjun Du
Papers
1
Total Citations
3
H-Index
1
About
Xiangjun Du is a leading researcher in intelligent robotics and reinforcement learning, with a primary focus on advancing automation in industrial sorting applications. His most impactful work centers on developing optimal neural network control models for robotic manipulators, particularly in the challenging domain of coal gangue sorting. Du’s major contribution lies in improving Deep Q-Network (DQN) algorithms to achieve precise trajectory control, addressing significant limitations in traditional models such as slow convergence and instability. His 2025 paper on “Improved DQN-Based Intelligent Trajectory Control for Coal Gangue Sorting Robotic Manipulators” has garnered early attention with 3 citations, reflecting its emerging influence in the field. By integrating reinforcement learning with real-time environmental interaction, Du has pioneered methods that enhance robotic accuracy and efficiency in hazardous waste-sorting environments. His work not only pushes the boundaries of intelligent motion control but also offers practical solutions for automating labor-intensive and dangerous tasks in mining and manufacturing. Du’s research continues to inspire innovations at the intersection of artificial intelligence and industrial robotics, making him a notable figure in the development of smarter, safer automation systems.
Research Focus
Key Achievements
Top Papers
- 1