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This project aims to improve endovascular procedures like TAVI using robotic assistance. It focuses on real-time path-planning algorithms and ultrasonic and electromagnetic sensors to enhance catheter navigation, aiming to increase surgical success rates and minimise long-term side effects.

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lukuky64/SaC_CATHETER_NAV

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Sensors and Control Repository

Getting Started

Prerequisites

For easy installation from Ubuntu terminal, run this like so: ./install_dependencies.sh

For individual installation:

Details
sudo sh -c 'echo "deb http://packages.ros.org/ros/ubuntu $(lsb_release -sc) main" > /etc/apt/sources.list.d/ros-latest.list'
sudo apt-key adv --keyserver 'hkp://keyserver.ubuntu.com:80' --recv-key C1CF6E31E6BADE8868B172B4F42ED6FBAB17C654
sudo apt-get update
sudo apt-get install ros-noetic-desktop-full
  • Catkin
sudo apt-get install ros-noetic-catkin
  • pcl-conversions
sudo apt-get install ros-noetic-pcl-conversions
  • Python3
sudo apt-get update
sudo apt-get install python3
  • Tensorflow
pip3 install tensorflow
  • libopencv-dev
sudo apt-get install libopencv-dev

Clone the Repository in your catkin workspace and build:

Details
cd ~/catkin_ws/src
git clone git@github.com:lukuky64/SaC_CATHETER_NAV.git

Navigate to your Catkin workspace and build the project:

cd ~/catkin_ws
catkin_make
source devel/setup.bash

Running the Main Simulation

Details
  1. Set the environment variable (replace the path in the quotes with the path to your data folder):

The structure of data folder should be (where * should be integers from 0-n):

root/
└── Aortic_catheter_project/
    ├── Data2_Soft_pullback_1/
    │   ├── Contours/
    │   │   └── *.txt
    │   ├── EM/
    │   │   └── *.txt
    │   └── Images/
    │       └── *.jpg
    └── unused_folders/
export MY_PATH="/home/lukuky64/Desktop/Aortic_catheter_project"
  1. Launch the main simulation:
roslaunch main main.launch

  1. Unpause publisher:
rosservice call /set_paused "data: false" 

To pause again:

rosservice call /set_paused "data: true" 

About

This project aims to improve endovascular procedures like TAVI using robotic assistance. It focuses on real-time path-planning algorithms and ultrasonic and electromagnetic sensors to enhance catheter navigation, aiming to increase surgical success rates and minimise long-term side effects.

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