Papers
6
Total Citations
83
H-Index
3
About
Ihab S. Mohamed is a robotics and control systems researcher whose work centers on autonomous navigation, sampling-based model predictive control, and visual servoing. He is best known for developing and advancing the Model Predictive Path Integral (MPPI) framework, a powerful approach to real-time trajectory optimization for robotic systems operating in complex, uncertain environments. His landmark contribution, the Log-MPPI control strategy (2022), addressed a critical limitation of conventional MPPI by improving trajectory feasibility in highly cluttered spaces, earning 38 citations and establishing him as a notable voice in the field. Building on this, his GP-Guided MPPI work (2023) incorporated Gaussian Process models to provide global navigational guidance, significantly enhancing efficiency in unknown environments and accumulating 20 citations. Mohamed has also made meaningful strides in visual servoing, introducing the MPPI-VS framework that bridges Path Integral optimal control theory with both image-based and position-based visual servoing systems. Collectively, his research pushes the boundaries of autonomous ground vehicle (AGV) navigation and constrained robotic control, offering practical, mathematically grounded solutions that are increasingly influential across the robotics research community.
Research Focus
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Top Papers
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