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About

Ahmed Maged is a robotics researcher whose work bridges the gap between theoretical path planning and practical robot execution. His primary research areas include mobile robot navigation, trajectory smoothing, and system maintenance optimization. Maged's most significant contribution is his work on "Space deformation based path planning for Mobile Robots" (2021, 18 citations), which introduced an innovative approach to generating feasible paths in complex environments. He further advanced the field with his "Path Smoothing Algorithm Using Thin-Plate Spline" (2021, 4 citations), addressing the critical challenge of converting sharp, kinodynamically infeasible paths into smooth, executable trajectories—a vital step for stable and efficient robot movement. Beyond navigation, Maged has explored system reliability through "Time Between Events Monitoring for Imperfect Maintained Systems" (2021, 4 citations), applying his insights to robotic systems, and demonstrated expertise in soft computing with "Design and Analysis of Experiments in ANFIS Modeling of a 3-DOF Planner Manipulator" (2018, 4 citations). His work is essential reading for students and researchers interested in practical robotics, offering solutions that directly improve real-world robot performance and longevity.