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About

Sijie Yan is a robotics and automation researcher whose work spans visually-guided robotic manufacturing, computer vision, and human-robot collaboration. Best known for foundational contributions to robotic grinding of complex aerospace components, Yan's early work tackled critical challenges in hand-eye calibration and 3D shape matching — two essential prerequisites for precision blade manufacturing. His 2015 paper on hand-eye calibration, which has garnered 100 citations, established robust methods for aligning sensor and end-effector coordinate frames, while his 2016 work on 3D blade surface matching (84 citations) advanced the field by addressing limitations in existing shape registration techniques, collectively helping accelerate the adoption of robots in demanding industrial environments. More recently, Yan has extended his expertise into smart manufacturing and digital twin systems, proposing vision-based frameworks for safe human-robot collaborative assembly (33 citations). His 2024 research on conformal slit mapping for spiral coverage path planning demonstrates a growing interest in geometric computation applied to robotic machining. Across his body of work, Yan consistently bridges theoretical computer vision with real-world industrial automation, making his research particularly valuable for engineers and researchers pursuing intelligent, adaptive manufacturing systems.