Papers
6
Total Citations
40
H-Index
4
About
Xingmao Shao is a leading researcher at the intersection of robotics, control theory, and industrial cybersecurity, with a focus on advancing smart manufacturing and intelligent systems. His core contributions lie in robot dynamics modeling and parameter identification, where he has developed innovative methods to address the challenges posed by non-deterministic factors such as joint friction, clearance, and flexibility. Notably, his work on a novel friction model and constrained differential evolution for parameter extraction (2023, 11 citations) and his semiparametric deep learning approach for inverse dynamics (2020, 11 citations) have provided critical foundations for high-precision robotic control in smart city and factory applications. Shao has also pioneered research on feedforward control based on dynamics parameter identification (2020, 9 citations), enabling more accurate motion performance. Beyond traditional robotics, he has made significant strides in securing networked industrial robots against data integrity attacks and covert threats, proposing detection strategies that combine physical dynamics with deep learning (2024, 3 citations). His work on teleoperation assisted by composite virtual fixtures (2024, 2 citations) further demonstrates his versatility in human-robot interaction. With a growing citation impact, Shao’s research is shaping the future of safe, efficient, and intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6