Zhikai Shen
Papers
2
Total Citations
45
H-Index
2
About
Zhikai Shen is making transformative contributions to the field of industrial robotics, with a primary focus on high-precision dynamics modeling and advanced calibration techniques. His most cited work introduces a groundbreaking Physics-Informed Neural Network (PINN) approach that overcomes the limitations of traditional linearized models by capturing complex nonlinear friction dynamics—a critical advancement for high-performance robot control. This paper has already garnered 30 citations, reflecting its immediate impact on the robotics community. Shen further distinguishes himself through a unified framework for in-situ calibration and synchronous identification, which elegantly solves the persistent problem of coupled motor-side uncertainties by integrating bedplate wrench sensing with motor current data. This composite sensing methodology, cited 15 times, enables decoupled parameter identification for both link-side and motor-side dynamics. By addressing fundamental challenges in robot modeling and identification, Shen's work directly enables more precise, reliable, and high-performance industrial automation systems. His research represents a significant step forward in bridging the gap between theoretical dynamics models and real-world robotic applications.
Research Focus
Key Achievements
Top Papers
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- 2