Papers
2
Total Citations
4
H-Index
1
About
Jie Pu is a robotics researcher specializing in the motion planning and cooperative control of multi-manipulator systems, particularly for heavy-duty industrial applications like tunnel drilling. His work addresses critical challenges in large-scale, multi-arm robotic operations by integrating advanced kinematic modeling with intelligent control strategies. In his most cited paper (2025, 3 citations), Pu introduces a novel approach that combines an enhanced Denavit-Hartenberg method with radial basis function neural networks to establish precise forward and inverse kinematics for tunnel drilling robots. He further develops an improved genetic algorithm for smooth, efficient motion planning in cooperative multi-arm tasks. Another notable contribution (2025, 1 citation) extends this work to cooperative active disturbance rejection control, leveraging mechanical-electrical-hydraulic joint simulation to enhance robustness in real-world conditions. Pu’s research is pivotal for advancing automation in construction and mining, where precise, coordinated robotic movements are essential for safety and productivity. His innovative fusion of neural networks, optimization algorithms, and control theory marks him as an emerging leader in intelligent robotic systems for challenging environments.
Research Focus
Key Achievements
Top Papers
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