A Master-to-Simulation Interface with Demonstration-Trajectory Guidance for Vascular Intervention

Mingyu Zou; Shuxiang Guo; Sheng Cao; Pengcheng Li; Jian Guo; Bin Wang; Mingchao Din

cs.ROOct 14, 2026

A Master-to-Simulation Interface with Demonstration-Trajectory Guidance for Vascular Intervention

Authors: Mingyu Zou, Shuxiang Guo, Sheng Cao, Pengcheng Li, Jian Guo, Bin Wang, Mingchao Din

Organizations: Beijing Institute of Technology, Beijing 100081, China · Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China · School of Life Science and the Key Laboratory of Convergence Medical Engineering System and Healthcare Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China · Shenzhen Institute of Advanced Biomedical Robot Company Limited, Shenzhen 518000, Guangdong Province, China ·

Abstract

Robotic vascular intervention can reduce radiation exposure to physicians and improve manipulation stability, but effective guidance remains challenging because conventional fluoroscopic images provide limited spatial information. This paper developed a vascular intervention interactive system that connects a real robotic master controller with the SOFA(Simulation Open Framework Architecture) physical simulation platform and reused successful operation trajectories as visual guidance in physical phantom experiments. Experiments were conducted to validate the master-to-SOFA control loop and to compare three guidance conditions: no guidance, centerline guidance, and successful-trajectory guidance. The control validation results showed reliable command delivery under velocity-mode operation. In the physical phantom experiment, centerline guidance showed no obvious improvement over no guidance. In contrast, successful trajectory guidance provided a clear performance benefit. Compared with the no-guidance condition, it reduced task completion time, collision number, rollback number, and rotation number by approximately 66.8%, 34.3%, 55.6%, and 66.7%, respectively. These results indicate that successful trajectories obtained under instrument-vessel interaction can provide practical guidance cues, offering a simple and effective visual assistance strategy for robotic vascular intervention experiments.

Figures & tables

Fig. 1: Overview of the interactive intervention system.

Fig. 2: Master manipulator of vascular intervention robot.

Fig. 3: Comparison of physical phantom guidance under three guidance conditions.

$N_{cmd}$ : number of valid commands; $R_{c}$ : command-to-pulse success rate.
Mode	Task	$N_{cmd}$	$R_{c}$ (%)	$\bar{\tau}$ (ms)	Std. of $\tau_{i}$ (ms)	Max. $\tau_{i}$ (ms)
Velocity	Guidewire translation	300	100.00%	20.25	32.40	136.70
Velocity	Guidewire rotation	288	100.00%	15.28	31.96	173.39
Velocity	Catheter translation	259	100.00%	27.10	38.17	155.54
Velocity	Catheter rotation	270	100.00%	14.50	30.36	156.75
Velocity	Combined operation	1106	100.00%	21.64	36.94	211.55

TABLE I: Master-to-SOFA control validation results under velocity-mode operation

Condition	$T_{c}$ (s)	$N_{col}$	$N_{back}$	$N_{rot}$
No guidance	$20.8\pm 4.7$	$7.0\pm 1.5$	$3.6\pm 1.3$	$3.9\pm 1.1$
Centerline guidance	$18.4\pm 4.1$	$7.1\pm 1.0$	$3.3\pm 0.7$	$3.8\pm 0.9$
Successful trajectory guidance	$6.9\pm 1.4$	$4.6\pm 0.5$	$1.6\pm 0.5$	$1.3\pm 0.5$

TABLE II: Physical phantom guidance results under different guidance conditions

Robotic Endovascular Intervention

Robotic endovascular intervention uses robotic systems to navigate vascular instruments and perform procedures with greater precision, control, or autonomy.

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Oct 14, 2026J/K move · Enter open · S save

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+2

Key Laboratory of Convergence Biomedical Engineering System and Healthcare Technology, Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China. · Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China. · Department of Peripheral Vascular Intervention, Aerospace Center Hospital, School of Life Science, Beijing Institute of Technology, Beijing 100081, China.

Virtual Reality Robotic Endovascular Intervention Robotic Control

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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.

Abstract

Robotic Endovascular Intervention

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