Xingbo Yao
Papers
5
Total Citations
130
H-Index
3
About
Xingbo Yao is an emerging researcher at the forefront of agricultural robotics and autonomous systems, with a focused expertise in navigation, localization, and path planning for robots operating in complex farming environments. His most influential contribution, a 2023 comprehensive review on crop row detection methods for agricultural robots and autonomous vehicles, has rapidly accumulated 94 citations, establishing itself as a foundational reference in the field. This work systematically addresses the challenges of navigating dynamic row-crop fields, synthesizing detection methods that underpin precision agriculture applications worldwide. Building on this foundation, Yao has made significant strides in greenhouse robotics, developing adaptive navigation frameworks that integrate improved LiDAR-based mapping algorithms — specifically enhanced LeGO-LOAM — with robust path planning strategies such as OpenPlanner. His more recent 2025 work extends these contributions to unstructured agricultural environments, employing multi-sensor fusion and stable feature localization for reliable obstacle detection. Collectively, Yao's research addresses a critical bottleneck in agricultural automation: enabling robots to perceive, map, and navigate reliably amid the unpredictable conditions of real-world farming. With over 130 cumulative citations across a concise but impactful publication record, Yao represents a compelling voice in the next generation of smart agriculture researchers.
Research Focus
Key Achievements
Top Papers
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