Papers
2
Total Citations
13
H-Index
2
About
Arya Saboury is a researcher whose work bridges embedded systems, robotics, and safe autonomous navigation. His early contributions focused on real-time control, demonstrated by his work on a FPGA-based mobile robot (MRTQ) that used fuzzy algorithms for path tracking and obstacle avoidance, a project that has garnered foundational citations. More recently, Saboury has advanced the field of autonomous systems by addressing a critical challenge: reliable anomaly detection in dynamic environments. His 2025 paper, "Uncertainty-Aware Real-Time Visual Anomaly Detection With Conformal Prediction in Dynamic Indoor Environments," introduces a novel framework that combines unsupervised deep learning with conformal prediction. This approach not only identifies visual anomalies in real-time but also quantifies the uncertainty of its detections, a crucial step for ensuring safety in settings like crowded university hallways. By integrating uncertainty awareness into visual anomaly detection, Saboury’s work enhances the trustworthiness of autonomous navigation systems, marking a significant contribution to the development of safer, more robust robots for human-centered environments.
Research Focus
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Top Papers
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