Fatemeh Jamshidi
Papers
5
Total Citations
28
H-Index
4
About
Fatemeh Jamshidi’s research lies at the intersection of robotics, control systems, and artificial intelligence, with a focus on enabling robots to navigate and operate autonomously in complex, unstructured environments. Her work addresses fundamental challenges in mobile robot navigation, trajectory tracking, and manipulator control by integrating advanced learning algorithms with robust control theory. In her most cited work, Jamshidi introduced a novel feature selection method that adapts to task environments for robot control, and she developed a framework combining recurrent neural networks with deep learning for vision-based off-road navigation—enabling robots to follow trajectories while avoiding pits and maintaining balance on uneven terrain. Her contributions extend to indoor trajectory tracking using depth data from Kinect cameras, offering scalability and robustness against uncertainty. With over 28 citations across her key papers, Jamshidi has also explored fuzzy adaptive observers for tennis ball trajectory estimation and designed a whale optimization algorithm-based interval type-II fuzzy fractional-order controller for two-link robot arms. Her work is notable for its practical integration of learning and control, advancing the reliability and adaptability of autonomous robotic systems in real-world settings.
Research Focus
Key Achievements
Top Papers
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