Papers
17
Total Citations
162
H-Index
7
About
Vijyant Agarwal’s research lies at the intersection of advanced control theory, stochastic systems, and surgical robotics, with a particular focus on enhancing the precision and stability of robotic manipulators in uncertain environments. His most impactful work, “Disturbance estimator as a state observer with extended Kalman filter for robotic manipulator” (34 citations), established a foundational framework for combining disturbance estimation with nonlinear state observation. He further advanced this line of inquiry by developing a generalized optimal unscented Kalman filter state observer-controller (UKFOC) for stochastic dynamical systems, addressing critical stability challenges posed by random noise in industrial plants. A particularly notable contribution is his work on tremor estimation and removal in robot-assisted surgery, where he applied Lie-group and Lie-algebra theory with an extended Kalman filter to model and mitigate physiological tremor in surgical robots—a problem of direct clinical relevance. Agarwal also contributed to the practical side of surgical robotics by developing a preoperative planning simulator with haptic feedback for the Raven-II platform, bridging the gap between simulation and real-world telesurgery. With over 120 total citations across his publications, his research continues to influence both theoretical developments in stochastic control and applied innovations in robot-assisted surgery.
Research Focus
Key Achievements
Top Papers
- 1
- 2Trajectory planning of redundant manipulator using fuzzy clustering method21 citations · 2011
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- 5Comparative Analysis For Kinematics Of 5-DOF Industrial Robotic Manipulator12 citations · 2015
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- 8Preoperative planning simulator with haptic feedback for Raven-II surgical robotics platform7 citations · 2016
- 9
- 10Intelligent control of four DOF robotic arm7 citations · 2016