Jing Shi

North Dakota State University, University of Cincinnati

Papers

Total Citations

H-Index

About

Jing Shi is a researcher whose work bridges foundational robotics theory with cutting-edge artificial intelligence applications. Her early contributions focused on localization performance in robotics, where she analyzed the statistical characteristics of landmark-based systems—a critical area for autonomous navigation. This foundational work, published in 2009, has garnered 8 citations and remains relevant for researchers developing robust positioning algorithms. More recently, Shi has turned her attention to the transformative potential of natural language models (NLMs) in industrial robotics. Her 2025 paper, already accumulating 5 citations, offers a comprehensive survey on how NLMs are revolutionizing human-robot interaction and operational efficiency in the pursuit of Industry 4.0 and 5.0. This forward-looking work examines the integration of large language models into manufacturing systems, enabling more intuitive programming and adaptive control. Shi's research trajectory demonstrates a keen ability to identify emerging technological synergies, from fundamental statistical methods to the latest AI-driven automation paradigms. Her work serves as a valuable resource for students and researchers interested in the convergence of robotics, machine learning, and human-centered automation.