Mohammed Jernaz
Papers
1
Total Citations
2
H-Index
1
About
Mohammed Jernaz is a robotics researcher whose work focuses on solving complex kinematic challenges in industrial automation. His primary research areas include inverse kinematics, artificial neural networks, and robotic manipulator control. Jernaz’s major contribution lies in applying neural network approaches to address the computationally difficult problem of inverse kinematics for high-degree-of-freedom robotic arms, as demonstrated in his most-cited work on a 6 DOF KUKA industrial robot. This paper, published in 2023, presents two neural network models that efficiently compute joint angles for precise end-effector positioning—a critical task in manufacturing and assembly. While his citation count is still growing, Jernaz’s work represents an important step toward making industrial robots more adaptable and easier to program, potentially reducing the need for complex mathematical modeling. His research bridges the gap between traditional robotics and modern machine learning, offering practical solutions for real-world automation challenges. As the field of intelligent robotics expands, Jernaz’s contributions are likely to gain increasing recognition among researchers and engineers seeking to enhance robotic dexterity and autonomy.
Research Focus
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Top Papers
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