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About

Dapeng Song is a leading researcher in the field of endovascular robotic systems, with a focus on enhancing the safety and precision of minimally invasive vascular interventions. His work centers on developing intuitive haptic feedback mechanisms, robotic catheter control, and real-time force sensing to improve surgical outcomes. Song’s major contributions include the design of a two-channel haptic force interface for endovascular robotic systems (13 citations), which provides immersive force feedback to surgeons, and a rotary encoder-based position transmission system (5 citations) that improves ergonomic control. He also pioneered methods for obtaining contact force between catheter tips and vascular walls (5 citations), crucial for preventing vessel damage, and developed visual algorithms for guidewire tracking (5 citations) to navigate complex anatomical environments. Additionally, Song designed adaptive clamping mechanisms and specialized graspers for robotic slave manipulators, addressing key limitations in catheter/guidewire handling. His work, totaling over 37 citations, has advanced the field by integrating haptics, vision, and mechanical design, making robotic catheterization safer and more accessible. Song’s innovations are pivotal for reducing surgeon radiation exposure and fatigue while enhancing procedural accuracy.