Zeinab Mahmoud Omer

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About

Zeinab Mahmoud Omer is a researcher specializing in robotics, intelligent control systems, and computational intelligence, with a particular focus on the intersection of adaptive algorithms and robotic motion control. Her work centers on solving one of robotics' most persistent challenges: the inverse kinematics problem, which addresses how robotic arms accurately determine joint configurations to achieve precise movement and positioning. Omer's most notable contributions involve the application of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to control the iconic PUMA 560 robot arm, implemented on microcontrollers in combination with omnidirectional wheel mobile robotic platforms. Her 2018 studies demonstrate how hybrid intelligent approaches — merging neural network learning capabilities with fuzzy logic reasoning — can significantly enhance accuracy and stability in robotic systems, areas she identifies as critical pain points for engineers and system designers. By integrating ANFIS with traditional PID controllers, her research offers practical pathways to more reliable robotic performance in real-world applications. While her published work currently holds a modest citation count of two per paper, her research addresses foundational problems in autonomous robotics and human-machine systems that carry growing relevance as intelligent robotics continues to expand across industrial and service sectors.