Khaled Hashim Mahmoud
Papers
2
Total Citations
16
H-Index
2
About
Khaled Hashim Mahmoud is a rising researcher in the field of industrial robotics, with a focused expertise in human-robot interaction (HRI) safety and collision detection. His work centers on developing intelligent, neural network-based methods to enhance the operational safety of multi-degree-of-freedom (DOF) manipulators. Mahmoud’s major contributions include pioneering a safety method for a 3-DOF industrial robot using a recurrent neural network (RNN), which is designed to detect undesired collisions on any robot link during operation. This work, published in 2023, has already garnered 10 citations, signaling its importance in the safety-critical domain of collaborative robotics. Expanding on this, he developed a pattern recognition neural network (PR-NN) classifier that categorizes torque signals into four distinct indicators—predicting no collision or identifying specific collision types—a contribution that has earned 6 citations in 2024. By integrating advanced neural architectures into real-time safety protocols, Mahmoud is helping to pave the way for safer, more reliable human-robot collaboration in industrial settings.
Research Focus
Key Achievements
Top Papers
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