Shujuan Li

Xi’an University, Xi'an University of Technology

Papers

Total Citations

H-Index

About

Shujuan Li’s research lies at the intersection of robotic manufacturing and bioinspired design, with a primary focus on improving automated deburring processes for high-voltage copper contacts. Her work addresses a critical industrial challenge: the removal of burrs—tiny, sharp protrusions left after machining—that can cause point discharge and device failure. Li pioneered a burr mathematical model and constructed a real burr image dataset, enabling advanced image denoising techniques for robotic vision systems. Her 2021 paper on BCOLTA-based denoising (4 citations) and her 2019 work on online burr video denoising via sparsifying transform (3 citations) provide foundational methods for enhancing the accuracy of robot-guided deburring. Beyond manufacturing, Li has contributed to climbing robotics with a 2021 paper (2 citations) introducing a spiny foot design featuring fluid-filled sacs, inspired by the gecko’s lamellar structure. This innovation ensures even force distribution across spines, improving grip on varied surfaces. Though her citation counts are modest, Li’s work is notable for its practical impact on industrial automation and its creative fusion of biological principles with engineering solutions—a promising direction for students interested in robotics, computer vision, or manufacturing.