# 基于2D图像反馈的自主血管内导航方法：感知与控制框架

> 原文链接：https://robotics.hellobike.com/papers/2026-10-15-2610-21481
> 频道：papers | 更新日期：2026-10-15 | 标签：arxiv, cs.RO
> 核心摘要：针对血管内自主导航的二维图像反馈难题，提出中心线图与规则控制框架，并在物理双器械平台的模拟平面图像闭环运行中验证。

# 基于2D图像反馈的自主血管内导航方法：感知与控制框架

::: v-pre
> 原文链接：[A 2D Image Feedback-Based Autonomous Endovascular Navigation Method: A Perception and Control Framework](https://arxiv.org/abs/2610.21481)（arXiv 2610.21481 · 发布日期：2026-10-15）

**A 2D Image Feedback-Based Autonomous Endovascular Navigation Method: A Perception and Control Framework**

针对血管内自主导航的二维图像反馈难题，提出中心线图与规则控制框架，并在物理双器械平台的模拟平面图像闭环运行中验证。

## 核心要点

- 动机：DSA和X射线透视用于判读解剖与器械运动，但难以转化为自主导航反馈。
- 方法：将血管掩膜转为带标签中心线图，把器械尖端投影到规划路径以估计进度、偏差和方向。
- 控制：规则控制器据此执行递送、分支选择、探查、回退和恢复。
- 实验：65次闭环运行覆盖三条路径，均达到至少0.95最大观测导丝进度，2次触发失败规则。

**验证范围与局限**：仅在物理双器械机器人平台上使用模拟器渲染平面图像的三条路径验证，临床图像序列和更广泛解剖仍待评估。

## 英文摘要 (Abstract)

In endovascular interventions, physicians rely on planar DSA and X-ray fluoroscopy to interpret vessel anatomy and device motion, yet converting these images into feedback for autonomous navigation remains difficult. A perception and control method is formulated for two-dimensional image feedback. Vessel masks are converted into a labeled centerline graph, and device-tip estimates are projected onto a planned path to obtain progress, deviation, and direction. A rule-based controller uses these quantities for delivery, branch selection, probing, rollback, and recovery. The method was evaluated on a physical dual-device robotic platform in 65 closed-loop runs using simulator-rendered planar images across three paths. All runs reached at least 0.95 maximum observed guidewire progress; two runs (3.1%) met the prespecified failure rule of three or more autonomous stop events. The results establish route-level performance on the tested platform; clinical image sequences and broader anatomy remain to be evaluated.

作者：Sheng Cao, Shuxiang Guo, 严勇敢, Jian Guo, Chunying Li, Bin Wang, Mingchao Ding, Yongxin Zhang, Yongwei Zhang, Pengfei Yang
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## 原始参考来源 (Sources)

- [arXiv 2610.21481：A 2D Image Feedback-Based Autonomous Endovascular Navigation Method: A Perception and Control Framework](https://arxiv.org/abs/2610.21481)