Hassan Diab

American University of Science and Technology

Papers

Total Citations

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About

Hassan Diab is a robotics researcher whose work bridges autonomous navigation and real-time aerial surveillance. His primary research areas include mobile robot path planning, genetic algorithms for optimization, and drone-based tracking systems. Diab’s most influential contribution is his 2019 paper on mobile robot path planning using genetic algorithms in static environments, which has garnered 26 citations. This work provides a comprehensive review of path planning optimization and introduces an algorithm that leverages genetic algorithms—pioneered by Goldberg—to solve complex navigation problems, offering a robust framework for autonomous robots to navigate efficiently without collisions. More recently, Diab has ventured into aerial robotics with a 2025 study on a real-time drone system for detecting, tracking, and following mobile robots within the ROS/Gazebo simulation environment. This work evaluates detection accuracy across varying distances, integrating OpenCV for object recognition and advancing drone-based surveillance capabilities. While still early in its citation impact, this research underscores Diab’s commitment to practical, real-world applications in security and automation. His work is particularly valuable for students and researchers exploring the intersection of evolutionary computation and robotic autonomy, offering foundational insights into both ground and aerial robotic systems.