Chengfan Gu
Papers
9
Total Citations
104
H-Index
7
About
Chengfan Gu is a researcher specializing in biomedical robotics, soft tissue biomechanics, and advanced filtering techniques, with a particular focus on applications in robotic-assisted minimally invasive surgery (RMIS). His work centers on developing sophisticated computational methods for real-time soft tissue characterization and deformation modeling, addressing critical challenges in achieving accurate haptic feedback and safe surgical automation. Gu's most significant contributions involve applying and extending Kalman filter frameworks — including Extended, Unscented, Iterative, and Random Weighting variants — to the nonlinear Hunt-Crossley contact model for dynamic soft tissue identification. His most cited work, an Extended Kalman Filter approach published in 2021, has garnered 40 citations and represents a landmark contribution to online tissue parameter estimation. Earlier foundational work from 2016 addressed the limitations of linear regression in nonlinear tissue modeling, while subsequent studies introduced adaptive filtering strategies and spatio-temporal Finite Element Method integration. Beyond parameter estimation, Gu has contributed to neural dynamics-based reaction-diffusion models for tissue deformation and developed master-slave robotic systems for needle indentation and insertion. Collectively, his research advances the precision, realism, and safety of next-generation surgical robotic systems, making his work highly relevant to both the robotics and biomedical engineering communities.
Research Focus
Key Achievements
Top Papers
- 1
- 2Iterative Kalman filter for biological tissue identification18 citations · 2023
- 3
- 4
- 5Master-slave robotic system for needle indentation and insertion8 citations · 2017
- 6
- 7
- 8
- 9