Mohamed Omar
Papers
5
Total Citations
44
H-Index
3
About
Mohamed Omar is a rising force in robotics, specializing in data-driven dynamic modeling and multi-criteria decision-making (MCDM) for industrial automation. His work bridges the gap between high-fidelity simulation and real-world robot control, focusing on serial manipulators. Omar’s major contributions include pioneering algorithms that extract nonlinear dynamic models—including complex friction effects—without requiring prior kinematic knowledge. His FAQT-2 method, a customer-oriented MCDM framework with statistical verification, has been applied to industrial robot selection, earning 18 citations since 2023. His 2022 study on data-driven dynamic modeling of serial manipulators (12 citations) introduced a three-step framework that enables controller design from simple trajectory data. More recently, his NL-WCS algorithm (2025) and R-SIEL physics-informed learning algorithm (2025) push the boundaries of model discovery, handling nonlinear friction and integrating physical constraints. With over 44 total citations and a trajectory of rapidly advancing techniques, Omar is shaping the future of intelligent, data-efficient robot control—critical for next-generation manufacturing and automation.
Research Focus
Key Achievements
Top Papers
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- 2A Framework for Data Driven Dynamic Modeling of Serial Manipulators12 citations · 2022
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