Pose State Perception of Interventional Robot for Cardio-cerebrovascular Procedures

TL;DR

This paper introduces a vision-based system for accurately perceiving the pose of interventional robots in cardio-cerebrovascular surgeries, enhancing control without additional sensors.

Contribution

It proposes a novel three-part framework combining deep learning, skeleton extraction, and geometric analysis for robot pose perception in complex vascular environments.

Findings

High reliability in trajectory tracking

Accurate pose state perception demonstrated

No additional sensors needed

Abstract

In response to the increasing demand for cardiocerebrovascular interventional surgeries, precise control of interventional robots has become increasingly important. Within these complex vascular scenarios, the accurate and reliable perception of the pose state for interventional robots is particularly crucial. This paper presents a novel vision-based approach without the need of additional sensors or markers. The core of this paper's method consists of a three-part framework: firstly, a dual-head multitask U-Net model for simultaneous vessel segment and interventional robot detection; secondly, an advanced algorithm for skeleton extraction and optimization; and finally, a comprehensive pose state perception system based on geometric features is implemented to accurately identify the robot's pose state and provide strategies for subsequent control. The experimental results demonstrate the proposed method's high reliability and accuracy in trajectory tracking and pose state perception.