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About

Abdelrahman Sayed Sayed is a researcher at the forefront of robotics and artificial intelligence, specializing in the kinematic modeling and control of complex robotic systems. His work bridges the gap between traditional robotics and modern AI, with a particular focus on parallel manipulators and multi-agent systems. Sayed’s most impactful contribution is his deep learning-based kinematic modeling of the 3-RRR parallel manipulator (43 citations), which offers a novel solution to the notoriously difficult inverse and direct kinematics of such systems. He further advanced this field through a neuro-fuzzy system for 3-DOF parallel robots (13 citations), providing a comparative analysis that enhances control precision. Demonstrating the real-world applicability of his research, Sayed led a proof-of-concept prototype for a centralized multi-agent mobile robot system, integrating a Hexapod and a wheeled robot for SLAM and navigation in COVID-19 field hospitals (30 citations). His experimental modeling of a Hexapod robot using AI (6 citations) further showcases his commitment to intelligent, autonomous systems. With a growing citation count and a focus on solving practical challenges in robotics, Sayed is establishing himself as a key innovator in AI-driven robotic control and multi-robot coordination.