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arXiv · 2610.19413

A VR Training System for Robot-Assisted Vascular Intervention with Dual-Mode Control and Model-Based Interaction Response

Yining Shang·Shuxiang Guo·Sheng Cao·Pengcheng Li·Bin Wang·Mingchao Ding
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Abstract

Virtual reality training for robot-assisted vascular intervention requires accurate robotic control and effective feedback on guidewire--vessel interactions. This paper presents a VR training system combining asynchronous dual-mode command mapping with sensorless interaction estimation. The system supports velocity-controlled navigation and position-driven incremental manipulation. Guidewire deformation and a Hunt--Crossley-type contact response are combined and temporally filtered to estimate interaction intensity, while trajectory-based visualization supports post-procedure review. Tests confirmed distinct command-dispatch patterns across three velocity gears and the position mode. During hybrid-scene navigation, the estimated response remained low during straight insertion and increased as the guidewire bent within the vascular model. Trajectory analysis yielded a model-derived cumulative trauma work (CTW) value of 15.166 in normalized units and localized regions of elevated interaction. In a preliminary study with 15 novices, physical-phantom task success increased from 53.3% to 93.3% after VR training, while mean completion time decreased from 132.4 s to 96.7 s. These results demonstrate the system's dual-mode operation, deformation-sensitive feedback, and potential to improve subsequent physical-phantom task performance.

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Yining Shang, Shuxiang Guo, Sheng Cao, Pengcheng Li, Bin Wang, Mingchao Ding. 2026-10-14. A VR Training System for Robot-Assisted Vascular Intervention with Dual-Mode Control and Model-Based Interaction Response. https://arxiv.org/abs/2610.19413

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