Menna M. Elnaggar

Papers

Total Citations

H-Index

About

Menna M. Elnaggar is a researcher whose work lies at the intersection of nonlinear dynamics, chaos theory, and intelligent robotics, with a particular focus on path planning for autonomous systems. Her most cited paper, "A Comparative Study of Different Chaotic Systems in Path Planning for Surveillance Applications" (2021, 5 citations), makes a significant contribution by systematically evaluating the performance of four chaotic systems—Lorenz, Arneodo, Liu, and Chen—in generating efficient, unpredictable trajectories for surveillance robots. Notably, Elnaggar was among the first to introduce the Arneodo, Liu, and Chen systems to this application domain, expanding the toolkit for chaotic path planning beyond the commonly used Lorenz system. Her work demonstrates how chaotic dynamics can enhance coverage, reduce predictability, and improve energy efficiency in autonomous navigation. With growing interest in bio-inspired and deterministic randomness for robotics, Elnaggar’s comparative analysis provides a valuable benchmark for future research. Her findings are particularly relevant for security, exploration, and environmental monitoring, where robust and unpredictable movement patterns are critical.