Sepideh Valiollahi

Aalborg University, Babol University of Medical Sciences

Papers

Total Citations

H-Index

About

Sepideh Valiollahi is a researcher whose work sits at the intersection of industrial automation, intelligent robotics, and real-time positioning systems. Her research focuses on enhancing the safety and efficiency of automated industrial environments through precise localization and adaptive machine learning. In her most cited work, "Experimental Evaluation and Modeling of the Accuracy of Real-Time Locating Systems for Industrial Use" (2024, 6 citations), Valiollahi provides critical insights into how RTLSs can monitor human and entity positions in real-time, enabling rapid responses to proximity-based hazards and unexpected events. This contribution is vital for the safe deployment of autonomous systems in factories and warehouses. Earlier, Valiollahi pioneered a novel approach to autonomous robot navigation in her 2012 paper, "A fuzzy Q-learning approach to navigation of an autonomous robot" (4 citations). By fusing fuzzy logic with Q-learning, she created a dynamic decision-making framework that handles environmental uncertainty while incorporating heuristic knowledge—a method that remains relevant for adaptive robotics. Her work demonstrates a consistent commitment to bridging theoretical machine learning with practical industrial applications, making her a notable figure in the fields of intelligent automation and cyber-physical systems.