Sameh F. Desouky

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About

Sameh F. Desouky is a leading researcher in intelligent robotics and autonomous systems, with a primary focus on developing adaptive control and learning algorithms for multi-robot environments. His major contributions lie at the intersection of fuzzy logic control and machine learning, where he has pioneered novel hybrid techniques to enable robots to learn and adapt without human supervision. His most cited work introduces a genetic-based fuzzy logic controller (GBFLC) for wall-following mobile robots, achieving 25 citations by demonstrating an iterative method to automatically tune membership functions and scaling parameters. He further advanced the field with a self-learning multi-robot system for pursuit-evasion games (13 citations), where robots operate in partially known environments with limited information. His notable achievement includes the development of a Q(λ)-learning fuzzy logic controller (12 citations), which combines reinforcement learning with function approximation to autonomously optimize controller parameters without pre-existing training data. Desouky’s work is instrumental in creating more intelligent, adaptable robotic systems capable of real-time decision-making in dynamic, uncertain environments, making him a key figure in the evolution of autonomous multi-robot coordination.