Saeed Ebadollahi

Iran University of Science and Technology

Papers

Total Citations

H-Index

About

Saeed Ebadollahi is a researcher at the intersection of robotics, computer vision, and intelligent control systems. His work focuses on integrating neural-network-based perception with real-time robotic manipulation, particularly for object tracking and autonomous interaction. His most cited paper, "An Integration of a Neural-Network-Based Computer Vision Model and a 2-DOF Object Tracker Robot" (2023), presents a novel system that combines deep learning object detection with a two-degree-of-freedom robotic arm, allowing users to select and track targets in dynamic environments. This contribution bridges the gap between high-level visual understanding and low-level motor control, offering a practical framework for applications in industrial automation, assistive robotics, and human-robot collaboration. While his citation count is still growing, Ebadollahi’s work demonstrates a clear commitment to advancing embodied AI—where machines not only see but act. His research is particularly relevant for students and engineers seeking to build end-to-end robotic systems that are both intelligent and responsive.