Uuganbayar Ganbold

Papers

Total Citations

H-Index

About

Uuganbayar Ganbold is a researcher focused on advancing autonomous robotics and computer vision, with particular emphasis on enabling intelligent navigation and environmental perception. His work tackles fundamental challenges in robotic autonomy, specifically in outdoor and agricultural settings. His most cited paper, "Local Texture Based Borderline Detection of Mowing" (2019, 2 citations), addresses the critical problem of automating mowing path planning by proposing a novel method to detect boundary lines between cut and uncut grass using local texture analysis—a contribution that directly supports the development of fully autonomous lawn maintenance robots. In "Horizontal line detection using genetic algorithm" (2019, 2 citations), Ganbold introduces an appearance-assumption-based approach for detecting horizontal or vanishing lines in images, a key capability for depth estimation and camera orientation in mobile robots and autonomous vehicles. While his citation counts reflect an emerging career, his work demonstrates practical, application-driven innovation at the intersection of texture analysis, genetic algorithms, and robotic perception. Ganbold’s research holds promise for advancing real-world autonomous systems in agriculture, landscaping, and navigation.