2 years in the past, I wrote A Information to Docker and ROS, which is one among my most steadily considered posts — probably as a result of it’s a difficult subject and folks had been looking for solutions. Since then, I’ve had the prospect to make use of Docker extra in my work and have picked up some new methods. This was lengthy overdue, however I’ve lastly collected my up to date learnings on this publish.
Not too long ago, I encountered an article titled ROS Docker; 6 the reason why they don’t seem to be a very good match, and I largely agree with it. Nonetheless, the fact is that it’s nonetheless fairly tough to make sure a reproducible ROS setting for individuals who haven’t spent years combating the ROS studying curve and are adept at debugging dependency and/or construct errors… so Docker remains to be very a lot a crutch that we fall again on to get working demos (and generally merchandise!) out the door.
If the article above hasn’t utterly discouraged you from embarking on this Docker journey, please get pleasure from studying.
Revisiting Our Dockerfile with ROS 2
Now that ROS 1 is on its ultimate model and approaching finish of life in 2025, I assumed it could be acceptable to rehash the TurtleBot3 instance repo from the earlier publish utilizing ROS 2.
Many of the large modifications on this improve must do with ROS 2, together with consumer libraries, launch information, and configuring DDS. The examples themselves have been up to date to make use of the newest instruments for conduct bushes: BehaviorTree.CPP 4 / Groot 2 for C++ and py_trees / py_trees_ros_viewer for Python. For extra info on the instance and/or conduct bushes, discuss with my Introduction to Habits Timber publish.
From a Docker standpoint, there aren’t too many variations. Our container structure will now be as follows:
Layers of our TurtleBot3 instance Docker picture.
We’ll begin by making our Dockerfile, which defines the contents of our picture. Our preliminary base layer inherits from one of many public ROS photos, osrf/ros:humble-desktop, and units up the dependencies from our instance repository into an underlay workspace. These are outlined utilizing a vcstool repos file.
Discover that we’ve arrange the argument, ARG ROS_DISTRO=humble, so it may be modified for different distributions of ROS 2 (Iron, Rolling, and many others.). Slightly than creating a number of Dockerfiles for various configurations, it is best to attempt utilizing construct arguments like these as a lot as attainable with out being “overly intelligent” in a means that impacts readability.
ARG ROS_DISTRO=humble
########################################
# Base Picture for TurtleBot3 Simulation #
########################################
FROM osrf/ros:${ROS_DISTRO}-desktop as base
ENV ROS_DISTRO=${ROS_DISTRO}
SHELL [“/bin/bash”, “-c”]
# Create Colcon workspace with exterior dependencies
RUN mkdir -p /turtlebot3_ws/src
WORKDIR /turtlebot3_ws/src
COPY dependencies.repos .
RUN vcs import
To construct your picture with a selected argument — let’s say you need to use ROS 2 Rolling as a substitute — you may do the next… offered that every one your references to ${ROS_DISTRO} even have one thing that accurately resolves to the rolling distribution.
docker construct -f docker/Dockerfile
--build-arg="ROS_DISTRO=rolling"
--target base -t turtlebot3_behavior:base .
I personally have had many points in ROS 2 Humble and later with the default DDS vendor (FastDDS), so I like to modify my default implementation to Cyclone DDS by putting in it and setting an setting variable to make sure it’s all the time used.
# Use Cyclone DDS as middleware
RUN apt-get replace && apt-get set up -y --no-install-recommends
ros-${ROS_DISTRO}-rmw-cyclonedds-cpp
ENV RMW_IMPLEMENTATION=rmw_cyclonedds_cpp
Now, we’ll create our overlay layer. Right here, we’ll copy over the instance supply code, set up any lacking dependencies with rosdep set up, and arrange an entrypoint to run each time a container is launched.
###########################################
# Overlay Picture for TurtleBot3 Simulation #
###########################################
FROM base AS overlay
# Create an overlay Colcon workspace
RUN mkdir -p /overlay_ws/src
WORKDIR /overlay_ws
COPY ./tb3_autonomy/ ./src/tb3_autonomy/
COPY ./tb3_worlds/ ./src/tb3_worlds/
RUN supply /turtlebot3_ws/set up/setup.bash
&& rosdep set up –from-paths src –ignore-src –rosdistro ${ROS_DISTRO} -y
&& colcon construct –symlink-install
# Arrange the entrypoint
COPY ./docker/entrypoint.sh /
ENTRYPOINT [ “/entrypoint.sh” ]
The entrypoint outlined above is a Bash script that sources ROS 2 and any workspaces which are constructed, and units up setting variables essential to run our TurtleBot3 examples. You should utilize entrypoints to do another forms of setup you would possibly discover helpful in your software.
#!/bin/bash
# Fundamental entrypoint for ROS / Colcon Docker containers
# Supply ROS 2
supply /choose/ros/${ROS_DISTRO}/setup.bash
# Supply the bottom workspace, if constructed
if [ -f /turtlebot3_ws/install/setup.bash ]
then
supply /turtlebot3_ws/set up/setup.bash
export TURTLEBOT3_MODEL=waffle_pi
export GAZEBO_MODEL_PATH=$GAZEBO_MODEL_PATH:$(ros2 pkg prefix turtlebot3_gazebo)/share/turtlebot3_gazebo/fashions
fi
# Supply the overlay workspace, if constructed
if [ -f /overlay_ws/install/setup.bash ]
then
supply /overlay_ws/set up/setup.bash
export GAZEBO_MODEL_PATH=$GAZEBO_MODEL_PATH:$(ros2 pkg prefix tb3_worlds)/share/tb3_worlds/fashions
fi
# Execute the command handed into this entrypoint
exec “$@”
At this level, it is best to have the ability to construct the total Dockerfile:
docker construct
-f docker/Dockerfile --target overlay
-t turtlebot3_behavior:overlay .
Then, we will begin one among our instance launch information with the appropriate settings with this mouthful of a command. Most of those setting variables and volumes are wanted to have graphics and ROS 2 networking functioning correctly from inside our container.
docker run -it --net=host --ipc=host --privileged
--env="DISPLAY"
--env="QT_X11_NO_MITSHM=1"
--volume="/tmp/.X11-unix:/tmp/.X11-unix:rw"
--volume="${XAUTHORITY}:/root/.Xauthority"
turtlebot3_behavior:overlay
bash -c "ros2 launch tb3_worlds tb3_demo_world.launch.py"
Our TurtleBot3 instance simulation with RViz (left) and Gazebo traditional (proper).
Introducing Docker Compose
From the previous few snippets, we will see how the docker construct and docker run instructions can get actually lengthy and unwieldy as we add extra choices. You may wrap this in a number of abstractions, together with scripting languages and Makefiles… however Docker has already solved this downside by way of Docker Compose.
In short, Docker Compose means that you can create a YAML file that captures all of the configuration wanted to arrange constructing photos and operating containers.
Docker Compose additionally differentiates itself from the “plain” Docker command in its means to orchestrate companies. This includes constructing a number of photos or targets throughout the identical picture(s) and launching a number of applications on the identical time that comprise a complete software. It additionally enables you to lengthen present companies to attenuate copy-pasting of the identical settings in a number of locations, outline variables, and extra.
The tip aim is that we’ve quick instructions to handle our examples:
- docker compose construct will construct what we’d like
- docker compose up
will launch what we’d like
Docker Compose permits us to extra simply construct and run our containerized examples.
The default title of this magical YAML file is docker-compose.yaml. For our instance, the docker-compose.yaml file seems as follows:
model: "3.9"
companies:
# Base picture containing dependencies.
base:
picture: turtlebot3_behavior:base
construct:
context: .
dockerfile: docker/Dockerfile
args:
ROS_DISTRO: humble
goal: base
# Interactive shell
stdin_open: true
tty: true
# Networking and IPC for ROS 2
network_mode: host
ipc: host
# Wanted to show graphical functions
privileged: true
setting:
# Wanted to outline a TurtleBot3 mannequin kind
- TURTLEBOT3_MODEL=${TURTLEBOT3_MODEL:-waffle_pi}
# Permits graphical applications within the container.
- DISPLAY=${DISPLAY}
- QT_X11_NO_MITSHM=1
- NVIDIA_DRIVER_CAPABILITIES=all
volumes:
# Permits graphical applications within the container.
- /tmp/.X11-unix:/tmp/.X11-unix:rw
- ${XAUTHORITY:-$HOME/.Xauthority}:/root/.Xauthority
# Overlay picture containing the instance supply code.
overlay:
extends: base
picture: turtlebot3_behavior:overlay
construct:
context: .
dockerfile: docker/Dockerfile
goal: overlay
# Demo world
demo-world:
extends: overlay
command: ros2 launch tb3_worlds tb3_demo_world.launch.py
# Habits demo utilizing Python and py_trees
demo-behavior-py:
extends: overlay
command: >
ros2 launch tb3_autonomy tb3_demo_behavior_py.launch.py
tree_type:=${BT_TYPE:?}
enable_vision:=${ENABLE_VISION:?}
target_color:=${TARGET_COLOR:?}
# Habits demo utilizing C++ and BehaviorTree.CPP
demo-behavior-cpp:
extends: overlay
command: >
ros2 launch tb3_autonomy tb3_demo_behavior_cpp.launch.py
tree_type:=${BT_TYPE:?}
enable_vision:=${ENABLE_VISION:?}
target_color:=${TARGET_COLOR:?}
As you’ll be able to see from the Docker Compose file above, you’ll be able to specify variables utilizing the acquainted $ operator in Unix based mostly techniques. These variables will by default be learn from both your host setting or by way of an setting file (often referred to as .env). Our instance.env file seems like this:
# TurtleBot3 mannequin
TURTLEBOT3_MODEL=waffle_pi
# Habits tree kind: May be naive or queue.
BT_TYPE=queue
# Set to true to make use of imaginative and prescient, else false to solely do navigation behaviors.
ENABLE_VISION=true
# Goal shade for imaginative and prescient: May be purple, inexperienced, or blue.
TARGET_COLOR=blue
At this level, you’ll be able to construct all the pieces:
# By default, picks up a `docker-compose.yaml` and `.env` file.
docker compose construct
# It’s also possible to explicitly specify the information
docker compose –file docker-compose.yaml –env-file .env construct
Then, you’ll be able to run the companies you care about:
# Deliver up the simulation
docker compose up demo-world
# After the simulation has began,
# launch one among these in a separate Terminal
docker compose up demo-behavior-py
docker compose up demo-behavior-cpp
The total TurtleBot3 demo operating with py_trees because the Habits Tree.
Organising Developer Containers
Our instance thus far works nice if we need to bundle up working examples to different customers. Nonetheless, if you wish to develop the instance code inside this setting, you have to to beat the next obstacles:
- Each time you modify your code, you have to to rebuild the Docker picture. This makes it extraordinarily inefficient to get suggestions on whether or not your modifications are working as meant. That is already an prompt deal-breaker.
- You may clear up the above by utilizing bind mounts to sync up the code in your host machine with that within the container. This will get us heading in the right direction, however you’ll discover that any information generated contained in the container and mounted on the host will probably be owned by
rootas default. You will get round this by whipping out thesudoandchownhammer, nevertheless it’s not needed. - All of the instruments chances are you’ll use for improvement, together with debuggers, are probably lacking contained in the container… until you put in them within the Dockerfile, which might bloat the dimensions of your distribution picture.
Fortunately, there’s a idea of a developer container (or dev container). To place it merely, this can be a separate container that permits you to truly do your improvement in the identical Docker setting you’d use to deploy your software.
There are various methods of implementing dev containers. For our instance, we’ll modify the Dockerfile so as to add a brand new dev goal that extends our present overlay goal.
Dev containers enable us to develop inside a container from our host system with minimal overhead.
This dev container will do the next:
- Set up further packages that we could discover useful for improvement, resembling debuggers, textual content editors, and graphical developer instruments. Critically, these is not going to be a part of the
overlaylayer that we are going to ship to finish customers. - Create a brand new person that has the identical person and group identifiers because the person that constructed the container on the host. It will make it such that every one information generated throughout the container (in folders we care about) have the identical possession settings as if we had created the file on our host. By “folders we care about”, we’re referring to the ROS workspace that comprises the supply code.
- Put our entrypoint script within the person’s Bash profile (
~/.bashrcfile). This lets us supply our ROS setting not simply at container startup, however each time we connect a brand new interactive shell whereas our dev container stays up.
#####################
# Improvement Picture #
#####################
FROM overlay as dev
# Dev container arguments
ARG USERNAME=devuser
ARG UID=1000
ARG GID=${UID}
# Set up further instruments for improvement
RUN apt-get replace && apt-get set up -y –no-install-recommends
gdb gdbserver nano
# Create new person and residential listing
RUN groupadd –gid $GID $USERNAME
&& useradd –uid ${GID} –gid ${UID} –create-home ${USERNAME}
&& echo ${USERNAME} ALL=(root) NOPASSWD:ALL > /and many others/sudoers.d/${USERNAME}
&& chmod 0440 /and many others/sudoers.d/${USERNAME}
&& mkdir -p /residence/${USERNAME}
&& chown -R ${UID}:${GID} /residence/${USERNAME}
# Set the possession of the overlay workspace to the brand new person
RUN chown -R ${UID}:${GID} /overlay_ws/
# Set the person and supply entrypoint within the person’s .bashrc file
USER ${USERNAME}
RUN echo “supply /entrypoint.sh” >> /residence/${USERNAME}/.bashrc
You may then add a brand new dev service to the docker-compose.yaml file. Discover that we’re including the supply code as volumes to mount, however we’re additionally mapping the folders generated by colcon construct to a .colcon folder on our host file system. This makes it such that generated construct artifacts persist between stopping our dev container and bringing it again up, in any other case we’d must do a clear rebuild each time.
dev:
extends: overlay
picture: turtlebot3_behavior:dev
construct:
context: .
dockerfile: docker/Dockerfile
goal: dev
args:
- UID=${UID:-1000}
- GID=${UID:-1000}
- USERNAME=${USERNAME:-devuser}
volumes:
# Mount the supply code
- ./tb3_autonomy:/overlay_ws/src/tb3_autonomy:rw
- ./tb3_worlds:/overlay_ws/src/tb3_worlds:rw
# Mount colcon construct artifacts for sooner rebuilds
- ./.colcon/construct/:/overlay_ws/construct/:rw
- ./.colcon/set up/:/overlay_ws/set up/:rw
- ./.colcon/log/:/overlay_ws/log/:rw
person: ${USERNAME:-devuser}
command: sleep infinity
At this level you are able to do:
# Begin the dev container
docker compose up dev
# Connect an interactive shell in a separate Terminal
# NOTE: You are able to do this a number of instances!
docker compose exec -it dev bash
As a result of we’ve mounted the supply code, you can also make modifications in your host and rebuild contained in the dev container… or you should utilize useful instruments just like the Visible Studio Code Containers extension to instantly develop contained in the container. As much as you.
For instance, when you’re contained in the container you’ll be able to construct the workspace with:
colcon construct
As a result of our quantity mounts, you’ll see that the contents of the .colcon/construct, .colcon/set up, and .colcon/log folders in your host have been populated. Because of this in the event you shut down the dev container and convey up a brand new occasion, these information will live on and can pace up rebuilds utilizing colcon construct.
Additionally, as a result of we’ve gone by way of the difficulty of constructing a person, you’ll see that these information are usually not owned by root, so you’ll be able to delete them in the event you’d like to scrub out the construct artifacts. You need to do this with out making the brand new person and also you’ll run into some annoying permissions roadblocks.
$ ls -al .colcon
complete 20
drwxrwxr-x 5 sebastian sebastian 4096 Jul 9 10:15 .
drwxrwxr-x 10 sebastian sebastian 4096 Jul 9 10:15 ..
drwxrwxr-x 4 sebastian sebastian 4096 Jul 9 11:29 construct
drwxrwxr-x 4 sebastian sebastian 4096 Jul 9 11:29 set up
drwxrwxr-x 5 sebastian sebastian 4096 Jul 9 11:31 log
The idea of dev containers is so widespread at this level that a typical has emerged at containers.dev. I additionally need to level out another nice sources together with Allison Thackston’s weblog, Griswald Brooks’ GitHub repo, and the official VSCode dev containers tutorial.
Conclusion
On this publish, you will have seen how Docker and Docker Compose may also help you create reproducible ROS 2 environments. This consists of the flexibility to configure variables at construct and run time, in addition to creating dev containers that can assist you develop your code in these environments earlier than distributing it to others.
We’ve solely scratched the floor on this publish, so be sure you poke round on the sources linked all through, check out the instance repository, and usually keep interested by what else you are able to do with Docker to make your life (and your customers’ lives) simpler.
As all the time, please be happy to succeed in out with questions and suggestions. Docker is a extremely configurable software, so I’m genuinely interested by how this works for you or whether or not you will have approached issues in a different way in your work. I would be taught one thing new!
Sebastian Castro
is a Senior Robotics Engineer at PickNik.
