Peikui Huang
Papers
2
Total Citations
49
H-Index
2
About
Peikui Huang is a leading researcher in agricultural robotics and autonomous navigation, with a focus on developing robust systems for unstructured outdoor environments. His work bridges precision control and deep learning to enable reliable robot operation in challenging settings like orchards and uneven terrain. Huang’s foundational contribution, the PI path tracking controller based on look-ahead distance for differential-drive tracked robots (2018, 28 citations), introduced a practical solution for GNSS-guided navigation, enhancing trajectory accuracy in field conditions. More recently, his deep-learning-based trunk perception system (2023, 21 citations) integrates depth estimation and Dynamic Window Approach (DWA) for robust navigation in orchards, overcoming GPS signal degradation and variable lighting. This work demonstrates how visual landmarks can replace unreliable satellite signals, a critical advance for agricultural autonomy. Huang’s research has direct implications for precision farming, reducing reliance on manual labor while improving operational safety. With a growing citation impact and a focus on real-world deployment, his contributions are shaping the next generation of intelligent, field-ready robots.
Research Focus
Key Achievements
Top Papers
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- 2