Jingming Liu

Hebei University of Technology, Northeastern University

Papers

Total Citations

H-Index

About

Jingming Liu is a researcher whose work lies at the intersection of robotics, automation, and spatial data processing. His most impactful contribution to date is the 2024 paper "Consideration of skewness in designing robotic compact storage and retrieval systems," which has already garnered 11 citations—a strong early indicator of its influence on warehouse logistics and robotic system design. In this work, Liu addresses a critical but often overlooked factor in automated storage: how load skewness affects system performance and layout efficiency, offering practical design guidelines that can reduce energy consumption and improve throughput. Earlier, Liu explored computer vision techniques with his 2012 paper on "3D Point Sets Matching Method Based on Moravec Vertical Interest Operator," laying groundwork for feature-based point cloud alignment. While that work has received modest attention (2 citations), it demonstrates his long-standing interest in geometric data processing. Liu’s research bridges theoretical modeling and applied robotics, making him a valuable voice for students and engineers seeking to optimize automated material handling systems. His growing citation trajectory suggests his skewness-aware design framework will become a standard reference in the field.