Hanjin Wen
Papers
2
Total Citations
101
H-Index
2
About
Hanjin Wen is a leading researcher in agricultural robotics, specializing in intelligent perception and autonomous manipulation for fruit harvesting. His work focuses on solving two critical challenges in nonstructural environments: reliable fruit detection and collision-free robotic navigation. Wen’s major contributions include developing deep neural network-based algorithms for grape cluster detection and pose estimation using binocular imagery, enabling harvesting robots to approach targets with precision and avoid damage. His 2021 paper on this topic has garnered 55 citations, reflecting its impact on the field. He also pioneered a collision-free path-planning method for six-degree-of-freedom serial harvesting robots, integrating energy optimization with artificial potential fields to navigate dynamic vineyard settings—work cited 46 times since 2018. Wen’s research bridges computer vision and robotics, advancing the practicality of automated harvesting. His notable achievements include designing robust fruit-detection systems that operate reliably under variable lighting and occlusion, a key step toward commercial adoption. For students and researchers, Wen’s work exemplifies how deep learning and control theory can transform agriculture, offering a roadmap for developing intelligent, efficient harvesting robots that reduce labor dependency and improve food production sustainability.
Research Focus
Key Achievements
Top Papers
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