Karim Davari Benam
Papers
2
Total Citations
11
H-Index
2
About
Karim Davari Benam is a researcher advancing the frontiers of medical robotics, with a focused expertise in image-guided control systems for minimally invasive surgery. His work addresses a critical challenge in robotic flexible needle steering: the inability to directly measure a needle’s orientation due to low-resolution imaging and the needle’s minute size. To overcome this, Benam developed a full-order high-gain observer, detailed in his most-cited 2019 paper (8 citations), which estimates the needle’s full state from limited visual feedback, enabling more precise navigation during procedures. He further extended this work by proposing a novel two-layer sliding mode model predictive control (SMPC) scheme (2018, 3 citations), which operates on two distinct timescales and models to compute feasible trajectories for 3D steering. This hierarchical approach combines the robustness of sliding mode control with the predictive optimization of MPC, significantly enhancing accuracy in complex tissue environments. Benam’s contributions are pivotal for the next generation of autonomous surgical tools, promising safer and more effective interventions in delicate operations like biopsies and brachytherapy.
Research Focus
Key Achievements
Top Papers
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