Papers
4
Total Citations
25
H-Index
3
About
Amir Panah is a robotics and autonomous systems researcher whose work centers on simultaneous localization and mapping (SLAM), mobile robot navigation, and advanced state estimation techniques. His research has consistently focused on enhancing the performance of Unscented Kalman Filter (UKF)-based SLAM systems by developing innovative hybrid computational approaches that compensate for inherent linearization errors. A recurring theme across his publications is the fusion of probabilistic filtering methods with intelligent computational techniques, including Radial Basis Function (RBF) neural networks and intuitionistic fuzzy logic, to produce more robust and accurate navigation solutions for autonomous wheeled mobile robots. His most cited work, an adaptive UKF framework incorporating intuitionistic fuzzy logic for concurrent localization and mapping (2022, 9 citations), represents the culmination of a research trajectory traceable back to his foundational 2013 paper on UKF-RBF hybrid SLAM (6 citations). Together, his publications have accumulated over 25 citations, reflecting a growing recognition of his contributions within the robotics community. Panah's research addresses real-world challenges in autonomous navigation, making his findings particularly valuable for engineers and researchers developing reliable indoor mobile robotic systems operating in unknown environments.
Research Focus
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Top Papers
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