Papers
12
Total Citations
178
H-Index
9
About
Farhad Shamsfakhr is a robotics and autonomous systems researcher whose work spans mobile robot navigation, indoor localization, and sensor fusion — areas that sit at the intersection of artificial intelligence, estimation theory, and industrial automation. His early contribution applying neural networks to obstacle avoidance in dynamic, unknown environments (2017, 40 citations) established his reputation for tackling real-world robotics challenges where sensing is limited and environments are unpredictable. Shamsfakhr subsequently made significant advances in robot localization, developing state estimators that leverage Ultra-Wideband (UWB) beacons, UHF-RFID technology, and Kalman filtering frameworks to achieve reliable indoor positioning — a notoriously difficult problem in cluttered or infrastructure-constrained settings. His 2021 papers on UWB-based localization and RFID-based Kalman smoothing (each earning 24 citations) reflect the depth and consistency of his impact in this domain. More recently, his research has extended into agricultural digital twins, deploying autonomous ground vehicles to overcome the limitations of fixed IoT installations. Across more than ten notable publications, Shamsfakhr's cumulative work demonstrates a coherent research vision: enabling robots to perceive, localize, and navigate reliably in the complex, dynamic environments where they are needed most.
Research Focus
Key Achievements
Top Papers
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- 3Robot Localisation Using UHF-RFID Tags: A Kalman Smoother Approach †24 citations · 2021
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- 7Robot Localisation using UHF-RFID Tags for Industrial IoT Applications10 citations · 2020
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