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About

Guanwen Xu is a leading researcher in agricultural robotics and precision navigation, with a focus on developing vision-based path extraction systems for greenhouse and field environments. His core contributions lie in applying advanced Hough transform techniques—specifically the prediction-point and median point Hough transforms—to enable autonomous navigation for cucumber and tomato-picking robots. These methods significantly improve the accuracy and robustness of navigation path detection in complex, unstructured agricultural settings. Xu’s most cited work, "Navigation path extraction for greenhouse cucumber-picking robots using the prediction-point Hough transform" (2020), has garnered over 110 citations, underscoring its influence on the field. His subsequent paper on tomato-cucumber greenhouse navigation has also been widely recognized with 62 citations. In his research on corn field navigation, Xu addressed the challenge of varying gray-scale characteristics across different growth stages, proposing an improved grayscale factor method that enhances path extraction speed and precision. Through these innovations, Xu has made substantial contributions to the automation of agricultural harvesting, enabling robots to operate reliably in dynamic crop environments.