About

Xiaopeng Huang is a robotics researcher whose work spans agricultural automation, aerial navigation, and brain-computer interfaces. His most impactful contribution addresses a critical bottleneck in agricultural robotics: the real-time detection of Zanthoxylum (prickly ash) peppers in complex, occluded environments. By developing an improved YOLOv5s architecture, Huang’s 2022 paper (34 citations) enables intelligent picking robots to accurately identify fruit hidden behind leaves and branches, directly improving harvesting efficiency. In autonomous aerial systems, his 2020 work on state estimation and mapping (22 citations) empowers micro aerial vehicles to navigate unknown environments using only onboard stereo cameras—a capability essential for search-and-rescue missions where GPS is unavailable. Huang also explores human-robot interaction, designing an SSVEP-based brain-computer interface (2021) that allows users to navigate telepresence robots through visual attention alone, offering new mobility solutions for individuals with disabilities. His research on virtual model control for two-wheeled-legged robots further demonstrates his versatility in dynamic balancing. Across these domains, Huang consistently addresses real-world constraints—occlusion, sensor limitations, and accessibility—making his work directly applicable to field robotics and assistive technology.

Research Focus

Key Achievements

3
H-Index
4
Papers
61
Total Citations
15
Avg Citations/Paper
🏆 Most Cited Paper
A Real-Time Zanthoxylum Target Detection Method for an Intelligent Picking Robot under a Complex Background, Based on an Improved YOLOv5s Architecture
34 citations · 2022
📈 Most Prolific Year: 2022 (2 Papers)
🤝 Key Collaborators: 16
🏛 Institutions: Gansu Agricultural University, Harbin Institute of Technology, Zhejiang University, Chinese University of Hong Kong, Shenzhen

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago