Mariam Al-Sagban

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About

Dr. Mariam Al-Sagban is a leading researcher in autonomous robotics, specializing in neural network-based navigation for wheeled mobile robots (WMRs). Her work focuses on enabling robots to navigate unstructured indoor environments without prior maps, using reactive control strategies that combine safety and efficiency. Her most influential contribution, the 2012 paper "Neural-based navigation of a differential-drive mobile robot" (24 citations), introduces a novel neural algorithm that allows a robot to reach a predefined goal while dynamically avoiding obstacles—a critical advance for real-world deployment in unknown spaces. This work, along with her 2016 follow-up "Neural Based Autonomous Navigation of Wheeled Mobile Robots" (12 citations), demonstrates the power of recurrent neural networks in handling complex, non-linear motion planning. Her Master's thesis (2012) further solidifies this foundation, exploring recurrent architectures for autonomous guidance. With a total of 38 citations across her key papers, Al-Sagban’s research bridges theoretical neural computation and practical robotics, offering a scalable solution for service robots, warehouse automation, and assistive technologies. Her contributions are particularly valued for their emphasis on real-time adaptability, making her a notable figure in the intersection of machine learning and mobile robotics.