R.C.3 : https://youtu.be/ZTsq-fo1JRo
R.C.5 : https://youtu.be/MNa5B4uPBEc
R.F.2 : https://youtu.be/exyVJGXXqWs
R.F.3 : https://youtu.be/r0D-aA17_5Q
R.F.4-R.F.5 : https://youtu.be/Kp2DKjgZOGI
R.F.6 : https://youtu.be/hazaTemEUx8
R.F.7 : https://youtu.be/lQY7RHeyr5o
R.F.8-R.F.9 : https://youtu.be/OxlJ8gVUlok
R.F.11 : https://youtu.be/UEtxdsRyZWE
R.F.12 : https://youtu.be/nQvFE3vZH98
R.F.16 : https://youtu.be/NkFpbyQ3oqQ
R.F.1 : https://youtu.be/jW5N3KHpleM
R.F.2 : https://youtu.be/910Vv9pmuNg
R.F.3 : https://youtu.be/zdT6yJBZk_A
R.F.4-R.F.5 : https://youtu.be/xhmPRty4eb4
R.F.6-8-9-11-12 : https://youtu.be/B7L6vY_rDgE
R.F.7 - Retour à la base : https://youtu.be/qT0P7P8uJD8
R.F.13 : https://youtu.be/Dh6XQaVc9tI
R.F.14-R.F.16 : https://youtu.be/wPQ9SxHwMvI
R.F.19 : https://youtu.be/PuRP_JvzIHs
R.C.1 : https://youtu.be/0A95FmTyeaU
R.C.2 : https://youtu.be/qLaY50ZoRwc
R.F.10 : https://youtu.be/eavHEMG_ItM
R.F.17-18 : https://youtu.be/P06RN5cEd_0
This command requires a valid ssh key that is registered in GitLab
git clone [email protected]:polytechnique-montr-al/inf3995/20221/equipe-106/INF3995-106.git --recursive
You have to install docker
sudo apt-get install docker-ce docker-ce-cli containerd.io
You will also need nvidia-docker if you have a nvidia gpu that is being used on ubuntu
distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
&& curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \
&& curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update
sudo apt-get install -y nvidia-docker2
sudo systemctl restart docker
You can then test it with this command
sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi
To get a detailed explanation of these steps please visit the official Nvidia documentation
You must also install docker-compose
sudo curl -L "https://github.com/docker/compose/releases/download/1.29.2/docker-compose-$(uname -s)-$(uname -m)" -o /usr/local/bin/docker-compose
The minimum required version of docker-composed is 1.25.* unless you are using nvidia-docker then you must at least install version 1.29.*
Additional information about the installation is provided:Docker-Compose documentation
In order to launch the application you can simply execute the shell script present in the repository
./start.sh
The script will launch and configure all of the Docker containers
If the file is not recognized as an executable please run this next command from the root of the project
chmod u+x start.sh
You can test each component of the project by running the start script with the test option (-t).
./start.sh -t
The test results will then be available in the terminal.
This next part explains how to manually launch and configure the containers as Docker devcontainers
If you are using an NVIDIA GPU you must install additionnal software for the simulation to display properly in the container
Follow the instructions provided here : https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html (NOTE it is not necessary to reinstall Docker)
You also need to modify the devcontainer.json file located in INF3995-106/embedded/.devcontainer/ by replacing the runArgs section with the following
"runArgs": [
//"--privileged",
//"--net=host",
"--device=/dev/ttyUSB0",
"--gpus=all", //Uncomment to use NVDIA GPU
"-e",
"DISPLAY=${env:DISPLAY}",
],To launch all the components of the project, you need the folders backend, frontend and embedded opened in vscode. Then using the Remote-Explorer extension from vscode, re-open all three folders in their respective container.
In the devcontainer for the frontend directory. Once the container is built, open a terminal window inside vscode and run the command
npm run serveOnce the compilation is done, the client can now be accessed in a web-browser at http://localhost:8080
In the devcontainer for the frontend directory. Once the container is built, open a terminal widow inside vscode and run the command
python3 server.pyThis will start the server and allow the client to connect to it.
In the devcontainer for the embedded directory.
Once the container is built, hit CTRL-SHIFT-P, select Tasks: Run Task and run the Install Argos3 Package Task
Afterwards, the same way as above, run the Compile Simulation Firmware Task
Then make sure the server and client are in their inital state (i.e. Have just been launched)
On the client, check the Simulation Toggle and press the Démarrer mission button
In the embedded devcontainer open a terminal inside vscode and run the commands
cd simulation
argos3 -c experiments/crazyflie_sensing.argosA simulation window should pop-up
You can now start the simulation using the Play button and send commands to the drone with the Lancer Mission and Terminer mission buttons on the client.
Make sure the CrazyRadio is connected to your computer.
In the devcontainer for the embedded directory, hit CTRL-SHIFT-P and select Remote-Containers: Rebuild Container
Once the container is re-built, hit CTRL-SHIFT-P, select Tasks: Run Task and run the Flash embedded firmware Task
The drones should now be flashed
Make sure the CrazyRadio is connected to your computer.
In the devcontainer for the server directory, hit CTRL-SHIFT-P and select Remote-Containers: Rebuild Container
Launch the client (See Launching the Client) Launch the server (See Launching the Server)
Make sure the drones have been flashed (See Flashing the Drone) and are powered on
On the client, do not check the Simulation Toggle button and press the Démarrer mission button
You should be able to select drone radio addresses in the top bar of the UI on the client. Selecting a Drone will allow you to send the identify command which will make its LED blink.
The main coding style follows the Google coding style. https://google.github.io/styleguide/?fbclid=IwAR2KlL02zVoscso_aX_n-sn4-7QVHfnk01A4TJjdtIatdC5YVyx6PAKSUPw
To apply the coding style, we used:
For the frontend: eslint
For the backend: yapf
For the embedded: clang-format and clang-tidy