Mugahed A. Al–antari
Kyung Hee University, Sejong University, Al-Farahidi University
Papers
4
Total Citations
38
H-Index
4
About
Mugahed A. Al-Antari is a researcher whose work bridges intelligent robotics, deep learning, and industrial fault diagnosis systems. His research spans two interconnected domains: robotic manipulation through artificial intelligence and the development of robust diagnostic frameworks for industrial robotic systems. Al-Antari's most widely recognized contribution applies deep reinforcement learning alongside a deep grasping probability network to enable anthropomorphic robotic hands to manipulate natural objects with human-like dexterity — a landmark study that has attracted 19 citations since its 2020 publication. This work reflects his commitment to advancing autonomous robotic capabilities through sophisticated machine learning architectures. More recently, Al-Antari has pioneered innovative fault diagnosis methodologies tailored for industrial robots, where reliability is operationally critical. His development of techniques combining Hierarchical Hyper-Laplacian Prior (HHLP) and Singular Spectrum Analysis (SSA) for analyzing rotary encoder signals represents a significant methodological advance, with related studies collectively accumulating nearly 20 citations. His SSA-Sparse MHD framework further demonstrates his expertise in detecting subtle defect signals that conventional methods may overlook. For students and researchers in robotics and intelligent systems, Al-Antari's portfolio offers valuable insights into both the enabling technologies of autonomous manipulation and the practical demands of industrial robotic maintenance.
Research Focus
Key Achievements
Top Papers
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