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Continuous Simulation Lab with Azure Pipelines (Linux)


After defining your robot behavior, it is important to ensure the robot running as expected as the project iterates. The traditional unit tests and integration tests usually use the static data to exercise your code. Continous simulation demonstrates how you make use of Azure Pipelines and ROS on Azure with Linux VM to run your code in Hardware-in-the-loop simulation and virtual environments. In this lab, we begin with self-driving car project with Gazebo simulation, test it on the rostest framework, and then cloud-host the continuous simulation on Azure Pipelines.

This lab uses the software developed by the Autonomous Racing Project Group of TU Dortmund. Credit goes to all the contributors.


  • Run and observe the autonomous racecar simulation locally.

  • Deploy and register a ROS on Linux virtual machine on Azure.

  • Integrate a GitHub project with Azure Pipelines.

  • Observe the simulation runs and test results on the pipeline.


  • An Microsoft Azure account from

  • An Azure DevOps account from

  • A GitHub account from

Exercise 1: Build And Run Autonomous Car Simulation Locally

  1. Fork ms-iot/ros_simulation_lab repository into your GitHub account.
  2. Follow this ROS Wiki page to install ROS Melodic on Ubuntu.
  3. Open a new shell. The below example assumes using bash.
# Clone the github project
git clone<your account>/ros_simulation_lab --recursive
cd ros_simulation_lab

# install required components
sudo apt update
rosdep update
rosdep install --from-paths catkin_ws/src --ignore-src -r -y
pip install circle-fit

# build it
cd catkin_ws
  1. Run the autonomous car simulation.
# source the ROS devel space.
source ./devel/setup.bat

# run the application
roslaunch src/ar-tu-do/ros_ws/launch/gazebo.launch world:=racetrack mode_override:=2

This launch file runs a racecar in a simulated track in Gazebo and runs autonomous driving.


Exercise 2: Run ROSTest With Autonomous Car Simulation Locally

  1. End the previous exercise and run the following rostest file:
# source the ROS devel space.
source ./devel/setup.bat

# run the rostest
rostest demo demo.test

This demo.test runs the same task as the previous exercise but in headless mode. Additonally, it runs a node to kick off a validation on the latest lap time. The demo package demonstrates an example how to organize your robot simulation with the rostest framework. Now let's move this exercise to cloud-hosted environment with Azure Pipelines.

Exercise 3: Provision Cloud CI Environment With Azure Pipelines

Task 1: Prepare Permission For Azure DevOps Agent Pool

  1. This module guides you how to prepare permission for the agent pool.
  2. Take a note of the personal access token (PAT).

Task 2: Deploy Virtual Machine and Register as Azure DevOps Build Agent

This ROS on Azure with Linux VM is a Azure quickstart template to help setup an Azure virtual machine with ROS installed.

  1. Navigate to the template. Click Deploy to Azure.
  2. A form will be brought to you and here are some important parameters for this exercise.
  3. Pipeline Provider: Select AzurePipelines to use Azure DevOps.
  4. Vsts Account: This is your Azure DevOps organization name. For example, this is the name of<name>.
  5. Vsts Personal Access Token: This is the PAT noted from the previous section.
  6. Vsts Pool Name: Leave it to Default to match the pool name in this exercise.


Task 3: Integrate Your GitHub Projects With Azure Pipelines

  1. Fork ms-iot/ros_simulation_lab repository into your GitHub account.
  2. The Integrate Your GitHub Projects With Azure Pipelines guides you how to create a pipeline for a GitHub project in Task 1 & 2. Use your fork as the target repository.
  3. Navigate to the "Existing Azure Pipelines YAML file" and select azure-pipelines-linux.yml.
  4. Now you should have a pipeline running (or ready to run).

Task 4: Observe the Build Summary and Test Results

  1. Click on a finished build and you will see a summary like: summary

  2. Check the Related and there is one artifact published, where you can find details logs for this run. In this example, you can also find the bag files for further analysis. logs

  3. Check the Test and coverage and you can find details test results by following the pass rate hyperlink. test_results