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About

Shangjunnan Liu’s research lies at the intersection of robotics, fluid dynamics, and intelligent systems, with a focus on solving real-world challenges in pipeline maintenance and electric vehicle infrastructure. His key contributions center on the design and optimization of specialized robots—both underwater pigging robots for oil and gas pipelines and mobile charging robots for electric vehicles. In his most-cited work (2020, 20 citations), Liu systematically optimized the structural parameters of a jet end in an underwater intelligent pigging robot, directly improving cleaning efficiency in harsh subsea environments. He further advanced pipeline robotics by analyzing how baffle plate and multi-jointed pneumatic sealing disc designs affect performance (2021, 3 citations; 2024, 3 citations). Notably, Liu’s 2023 paper (19 citations) introduced a novel hybrid algorithm—an improved gray wolf optimization integrated with A*—for path planning of mobile charging robots, addressing a critical gap in automated EV charging for aging parking lots. This work demonstrates his ability to merge bio-inspired optimization with practical robotics. With a growing citation record and a focus on tangible industrial applications, Liu is establishing himself as a researcher who bridges theoretical optimization and real-world robotic deployment.