Xiuli Guo

Papers

Total Citations

H-Index

About

Xiuli Guo is a pioneering researcher in forest industrial robotics, with a focus on automating labor-intensive tasks such as cone picking. Her work centers on integrating intelligent control systems—particularly neural networks and fuzzy logic—into robotic platforms to enhance precision and efficiency in forestry operations. Guo’s most cited paper, “An improved neural network based fuzzy self-adaptive KALMAN filter and its application in cone picking robot” (2009, 11 citations), introduces a novel RBF neural network model that optimizes robotic positioning and voltage input, significantly improving autonomous performance. Her complementary studies, including “Recent Development of Forest Industrial Robot in China” (2010, 9 citations) and “Research and application of rbf neural network in cone picking robot” (2009, 3 citations), provide critical overviews of China’s forestry automation challenges and technical solutions. Collectively, Guo’s contributions address key barriers in logging, stump excavation, and cone harvesting, aiming to reduce manual labor and boost productivity. Her work has laid foundational groundwork for intelligent forestry robotics, influencing both academic research and practical applications in the field.