Hassan Ashraf Elkholy

Papers

Total Citations

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About

Hassan Ashraf Elkholy is a robotics researcher whose work focuses on the intersection of deep learning and robotic manipulation. His most cited paper, "Solving Inverse Kinematics of a 7-DOF Manipulator Using Convolutional Neural Network" (2020), has garnered 14 citations, demonstrating his early impact in applying neural networks to complex robotic control problems. This work addresses the fundamental challenge of inverse kinematics—calculating joint configurations to achieve desired end-effector positions—for redundant manipulators, a problem critical to industrial automation and surgical robotics. By leveraging convolutional neural networks, Elkholy's approach offers a data-driven alternative to traditional analytical methods, potentially enabling faster and more adaptable solutions for high-degree-of-freedom systems. His research contributes to the broader field of intelligent robotics, where machine learning is increasingly used to simplify control algorithms and enhance robot autonomy. Elkholy's work is particularly relevant for students and researchers exploring how deep learning can bridge the gap between theoretical robotics and practical, real-time applications.