Papers
2
Total Citations
21
H-Index
2
About
Xu Shen is a robotics researcher whose work focuses on the design, optimization, and simulation of parallel kinematic machines—a class of robots prized for their high stiffness, speed, and precision. His major contributions lie in improving the dynamic performance of parallel robots through systematic parameter optimization. In his most cited work (2019, 15 citations), Shen tackled the critical challenge of balancing acceleration capacity, load stability, and cost by developing a driving system parameter optimization (DSPO) method for a 5-degree-of-freedom parallel machining robot. This approach enables engineers to select motors and drive components that maximize performance without unnecessarily high expense—a practical advance for industrial automation. He also contributed to the field of digital manufacturing with an NC code-based machining movement simulation method (2017, 6 citations), allowing virtual verification of parallel robot toolpaths before physical cutting. Shen’s research bridges theoretical kinematics and real-world machining constraints, making him a notable figure in parallel robotics for manufacturing. His work is particularly relevant for students and researchers interested in robot dynamics, mechatronic system design, and cost-effective automation solutions.
Research Focus
Key Achievements
Top Papers
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