Papers
4
Total Citations
37
H-Index
3
About
Haonan Yang is a rising researcher at the intersection of medical robotics and autonomous navigation, with key contributions in robotic ultrasound (US) systems and visual semantic navigation. His work addresses critical safety and precision challenges in minimally invasive surgery and cancer screening. Yang’s most cited paper (2022, 20 citations) introduces a virtual-force-guided intraoperative US scanning method that predicts optimal scanning planes during tumour resection, dynamically adapting to tissue deformation—a breakthrough for real-time surgical guidance. He further advances robotic breast ultrasound scanning (2025, 11 citations) by proposing smooth path planning and dynamic contact force regulation, directly tackling safety concerns in early breast cancer detection. In visual navigation, Yang’s HSPNav framework (2024, 4 citations) leverages hierarchical scene priors to enable robots to locate target objects in unseen environments, improving semantic understanding for real-world deployment. His innovative work on robot-assisted US calibration (2025, 2 citations) eliminates external trackers, streamlining workflows for clinical adoption. With a growing citation record and a focus on translating robotics into practical medical tools, Yang is shaping the future of autonomous, safe, and intelligent surgical assistance.
Research Focus
Key Achievements
Top Papers
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