N. Long

University of Transport and Communications

Papers

Total Citations

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About

N. Long is a researcher focused on the intersection of artificial intelligence and autonomous robotics, with a particular emphasis on deep learning applications for mobile systems. Their most-cited work, "Deep Learning-Based Object Tracking and Following for AGV Robot" (2023), has garnered 5 citations, demonstrating early recognition in the field. This paper introduces novel approaches for enabling automated guided vehicles (AGVs) to robustly track and follow objects in dynamic environments, leveraging convolutional neural networks to improve real-time performance and accuracy. Long's contributions address critical challenges in industrial automation, such as enhancing the reliability of AGVs in warehouse logistics and manufacturing settings. By integrating deep learning with traditional control systems, their work bridges the gap between theoretical AI advances and practical robotics applications. While still early in their career, Long's research shows promise in advancing autonomous navigation and human-robot interaction, with potential implications for smart factories and service robotics. Their focus on scalable, real-time solutions positions them as an emerging voice in the growing field of intelligent robotic systems.