Xinlong Xu

Papers

Total Citations

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About

Xinlong Xu is a researcher at the forefront of intelligent robotics and computer vision, with a primary focus on enhancing autonomous systems through advanced deep learning and sensor integration. His most cited work, "Target Localization and Grasping of NAO Robot Based on YOLOv8 Network and Monocular Ranging" (2023), addresses a critical challenge in robotics: improving target localization accuracy for grasping tasks. By fusing the YOLOv8 network—renowned for its rapid and precise object recognition—with monocular ranging techniques, Xu developed a method that significantly reduces positioning errors over longer distances, a common limitation in traditional visual systems. This innovative approach, which has garnered 4 citations, demonstrates his ability to bridge theoretical AI models with practical robotic applications. Xu’s contributions are particularly valuable for advancing human-robot interaction and autonomous manipulation, offering a scalable solution for real-world environments like manufacturing or assistive robotics. His work underscores a commitment to making robots more reliable and efficient, marking him as a promising contributor to the evolving field of intelligent systems.