Shenquan Huang
Papers
1
Total Citations
7
H-Index
1
About
Shenquan Huang is a researcher specializing in robotics and intelligent control systems, with a particular focus on multi-degree-of-freedom (MDOF) manipulators and trajectory optimization. His most cited work, published in 2023, introduces a novel approach to multi-objective trajectory optimization for 2-redundancy planar feeding manipulators, combining pseudo-attractor concepts with radial basis function neural networks. This research addresses the critical challenge of inverse kinematic model establishment, aiming to simultaneously improve trajectory smoothness, reduce mechanical jitter, and minimize energy consumption in robotic systems. With 7 citations to date, this work represents Huang's primary contribution to the field, demonstrating practical solutions for enhancing the efficiency and performance of redundant manipulators. His research holds significant implications for industrial automation and precision robotics, where smooth, energy-efficient motion is paramount. Huang's work continues to influence the development of intelligent control strategies for complex robotic systems, bridging the gap between theoretical optimization and real-world application in manufacturing and automation.
Research Focus
Key Achievements
Top Papers
- 1