Ahmed Badawy

University of Strathclyde, October University of Modern Sciences and Arts

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About

Ahmed Badawy’s research focuses on advancing autonomous robotic systems through novel control and learning methodologies. His major contributions lie in robot motion planning and autonomous structure assembly, where he pioneered the use of hyperboloid potential functions for path planning—an approach that avoids the unbounded forces of parabolic functions and the goal singularities of conic functions. He also introduced superquadric obstacle representation and quaternion-based orientation control for artificial potential fields, enabling more accurate and efficient assembly tasks. His work on developing hexapod locomotion through reinforcement learning, comparing PPO, DDPG, and SAC algorithms, marks a significant step toward self-learned robotic mobility. With over 20 citations across his most-cited papers, Badawy’s research has influenced both theoretical and practical aspects of robotics, particularly in safe and adaptive motion planning. His innovative use of geometric potential functions and reinforcement learning for complex locomotion demonstrates a sustained commitment to solving real-world robotic challenges, making his work a valuable resource for students and researchers in autonomous systems and intelligent control.