Xiaofan Du
Papers
1
Total Citations
5
H-Index
1
About
Xiaofan Du is a pioneering researcher in the field of robotic manipulation, with a primary focus on interaction force estimation and environmental stiffness exploration. His most-cited work, "Composite Disturbance Filtering for Interaction Force Estimation With Online Environmental Stiffness Exploration" (2024, 5 citations), addresses a critical challenge in minimally invasive surgery—accurately estimating the force between a robotic end effector and soft tissues. By developing a novel composite disturbance filtering method that simultaneously estimates interaction forces and identifies environmental stiffness in real time, Du has provided a foundational tool for enhancing the safety and precision of surgical robots. This contribution is particularly impactful for haptic feedback systems, where accurate force perception is essential for delicate procedures. Du’s work bridges the gap between theoretical control systems and practical medical applications, offering a robust solution for dynamic, unknown environments. His research not only advances robotic autonomy but also holds promise for improving patient outcomes in minimally invasive surgeries, marking him as a rising innovator in medical robotics and human-robot interaction.
Research Focus
Key Achievements
Top Papers
- 1