Papers
7
Total Citations
49
H-Index
4
About
Rohit Rana is a robotics researcher whose work sits at the intersection of advanced control theory, stochastic systems, and surgical robotics. His primary research areas include tremor estimation and removal in robot-assisted surgery, nonlinear state estimation using Kalman filtering, and the application of Lie group theory to robotic dynamics. Rana’s most cited work, "Tremor Estimation and Removal in Robot-Assisted Surgery Using Lie Groups and EKF" (17 citations), introduces a novel approach to modeling and canceling hand tremor in minimally invasive surgical robots by leveraging the mathematical elegance of Lie algebras. He further advanced stochastic control with his design of an optimal Unscented Kalman Filter state observer-controller (UKFOC), which addresses the challenge of random noise in highly nonlinear industrial systems. Rana has also pioneered the use of wavelet transforms for tremor removal and has applied large deviation principles to analyze rare-event exit probabilities in trajectory tracking robots. His integrated approach to parameter estimation, data compression, and noise elimination demonstrates a rare ability to bridge theoretical rigor with practical robotic applications. With a growing citation record and contributions to both surgical and industrial robotics, Rana is establishing himself as a thoughtful innovator in stochastic control and robot dynamics.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5Wavelet transformation based tremor removal3 citations · 2015
- 6
- 7