Papers
2
Total Citations
43
H-Index
2
About
Yusen Geng is an emerging researcher specializing in robotic welding automation, 3D machine vision, and intelligent manufacturing systems. His work sits at the intersection of computer vision and industrial robotics, with a particular focus on eliminating the time-consuming manual teaching and programming processes that have long hindered the widespread deployment of welding robots in complex manufacturing environments. Geng's most significant contributions center on developing sophisticated feature extraction algorithms and autonomous path planning methodologies for robotic welding systems. His 2024 paper on vision-guided welding for multi-blade wheel structures — a particularly challenging geometry in industrial manufacturing — has already garnered 24 citations, demonstrating rapid uptake within the robotics and manufacturing communities. Complementing this, his work on feature extraction over workpiece point clouds, which has accumulated 19 citations, addresses the critical challenge of automating weld seam identification from 3D data, moving beyond approaches that rely purely on predefined geometric assumptions. Though early in his career, Geng's research has already made a measurable impact on the field of intelligent robotic welding, offering practical pathways toward fully autonomous welding systems capable of adapting to complex, real-world workpiece geometries without human intervention.
Research Focus
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Top Papers
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