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About

Fatemeh Heidari’s research lies at the intersection of mobile robotics, human-machine interaction, and intelligent control systems, with a particular focus on autonomous navigation and assistive technologies. Her most impactful work introduces a human-inspired method (HIM) for point-to-point and path-following navigation of wheeled mobile robots in challenging outdoor farm settings. This fully integrated strategy—combining sensor data analysis, obstacle detection, avoidance, and goal seeking—has garnered 11 citations, establishing a foundation for practical agricultural robotics. Heidari has also advanced deep learning applications in rehabilitation, developing a convolutional neural network based on AlexNet architecture to classify hand movement patterns for use in wheelchairs, robots, and artificial hand prostheses. Her broader contributions include vision-based control of 6R robots and modeling of flexible-link manipulators, where she analyzed truncation errors in assumed shape modeling to improve dynamic accuracy. Through these diverse efforts, Heidari demonstrates a commitment to bridging theoretical robotics with real-world deployment, from farm navigation to human assistive devices, making her work relevant for researchers in autonomous systems, control engineering, and human-robot interaction.