Papers
2
Total Citations
32
H-Index
2
About
Yijun Fang is a rising researcher at the intersection of robotics and intelligent agricultural systems, whose work is shaping the future of human-machine collaboration and autonomous farming. Fang’s primary research areas include supernumerary robotic limbs (SRLs) and deep learning-based obstacle detection, with a focus on enhancing human operational capabilities and agricultural automation. In a landmark 2023 study, Fang developed a task-based human-robot collaboration control method for SRLs designed for overhead tasks—a novel wearable robotic system that augments human ability in complex, physically demanding environments. This work, which has garnered 24 citations, addresses critical safety and autonomy challenges in human-robot interaction. More recently, in 2024, Fang introduced YOLOv8-PSS, a lightweight obstacle detection model for unmanned agricultural vehicles, achieving 8 citations in a short time. This innovation tackles labor shortages in agriculture by enabling highly intelligent, accurate equipment technologies. Fang’s contributions are notable for their practical impact, bridging advanced robotics with real-world applications in industry and agriculture, and signaling a promising trajectory in autonomous systems research.
Research Focus
Key Achievements
Top Papers
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