Kaiwei Guo

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About

Kaiwei Guo is a researcher at the forefront of robotic additive manufacturing, with a primary focus on developing intelligent path planning strategies that bridge the gap between automation and geometric complexity. His most-cited work, "Hybrid path planning method based on skeleton contour partitioning for robotic additive manufacturing" (2023), has already garnered 22 citations, reflecting its timely impact on the field. In this study, Guo introduced a novel hybrid approach that leverages skeleton contour partitioning to optimize toolpaths for large-scale, freeform 3D printing, significantly improving deposition efficiency and surface quality while reducing material waste. This contribution addresses a critical bottleneck in robotic fabrication—how to seamlessly integrate geometric reasoning with motion control. Beyond this flagship paper, Guo’s research consistently explores the intersection of computational geometry, robotics, and material science, aiming to make additive manufacturing more adaptive and precise. His work is particularly notable for its practical relevance to industries such as aerospace and construction, where complex, custom parts are increasingly demanded. As a rising voice in advanced manufacturing, Kaiwei Guo is shaping the next generation of autonomous, intelligent production systems.