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Autonomous Racing Robot With an Arduino, a Raspberry Pi and a Pi Camera

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Autonomous toy racing car. CAMaleon team at the Toulouse Robot Race 2017. Humbavision team at IronCar. Medium article:

Video of the car:

The racing robot

Table of Contents

Detailed Presentation

We wrote an article on medium that detailed our approach. You can read it here

En français:

3D Models and Training Data

3D Models

3D models (we used

Note: the Battery Holder was designed for this External Battery

Training Data

IronCar and Toulouse Robot Race Datasets

We release the different videos taken with the on-board camera, along we the labeled data (the labels are in a pickle file) for IronCar and Toulouse Robot Race:

How to run everything ?

For installation, see section Installation.

Autonomous mode

  1. Compile and upload the code on the Arduino
cd arduino/
make upload
  1. Launch the main script on the Raspberry Pi, it will try to follow a line. All useful constants can be found in

Remote Control Mode

  1. You need a computer in addition to the raspberry pi
  2. Create a Local Wifi Network (e.g. using create ap)
  3. Connect the raspberry pi to this network (Wifi on RPI)
  4. Launch teleoperation server (it will use the port 5556)
python -m teleop.teleop_server
  1. Launch teleoperation client on your computer (you have to edit the raspberry pi IP in
python -m teleop.teleop_client
  1. Enjoy! You can now control the car with the keyboard.

How to train the line detector ?

  1. Record a video in the teleoperation mode:
python -m teleop.teleop_server -v my_video
  1. Convert the recorded video from h264 to mp4 using ffmpeg or MP4Box
MP4Box -add video.h264 video.mp4
  1. Split the video into a sequence of images
python -m train.split_video -i video.mp4 -o path/to/dataset/folder
  1. Label the data using the labeling tool:

  2. Rename the json file that contains the labels to labels.json and put it in the same folder of the dataset (folder with the images)

  3. Train the neural network (again please change the paths in the script)

python -m train.train -f path/to/dataset/folder

The best model (lowest error on the validation data) will be saved as cnn_model_tmp.pth.

  1. Test the trained neural network (you can use -i option to test it on a video)
python -m train.test -f path/to/dataset/folder -w cnn_model_tmp.pth


Recommended : Use an image with everything already installed

  1. You need a 16GB micro sd card (warning, all data on that card will be overwritten) WARNING: for a smaller sd card, you need to resize the image before writing it (this link and repo may help)

  2. Download the image here

Infos about the linux image: OS: Ubuntu MATE 16.04 for raspberry pi

Username: enstar

Password: enstar

Installed softwares:

  • all the dependencies for that project (OpenCV >= 3, PyTorch, ...)
  • the current project (in the folder RacingRobot/)
  • ROS Kinetic

Camera and SSH are enabled.

  1. Identify the name of your sd card using:
fdisk -l

For instance, it gives:

/dev/mmcblk0p1            2048   131071   129024   63M  c W95 FAT32 (LBA)
/dev/mmcblk0p2          131072 30449663 30318592 14,5G 83 Linux

In that case, your sd card is named /dev/mmcblk0 (p1 and p2 stand for partition).

  1. Write the downloaded image on the sd card.
gunzip --stdout ubuntu_ros_racing_robot.img.gz | sudo dd bs=4M of=/dev/mmcblk0
  1. Enjoy! The current project is located in RacingRobot/.

If you want to back up an image of a raspberry pi:

sudo dd bs=4M if=/dev/mmcblk0 | gzip > ubuntu_ros_racing_robot.img.gz

From Scratch

Update your pi

sudo apt-get update
sudo apt-get upgrade
sudo rpi-update

Arduino + Arduino Makefile + rlwrap + screen + MP4Box

sudo apt-get install arduino-core arduino-mk rlwrap screen gpac
  • Arduino 1.0.5
  • Arduino-Makefile
  • OpenCV >= 3
  • libserial-dev (apt-get)
  • Python 2 or 3


Python Packages

All the required packages (except pytorch and torchvision) can be found in requirements.txt, install them using:

pip install -r requirements.txt

In short:

  • PySerial
  • TQDM (for progressbar)
  • PyGame (for teleoperation)
  • Enum support (for Python 2)
  • ZeroMQ (for teleoperation)
  • Pytorch (you have to compile it from source for the RPI)
  • scikit-learn
  • scipy
pip install pyserial tqdm pygame enum34 scikit-learn scipy

ZeroMQ (Message Passing with sockets) for remote control mode

sudo apt-get install libzmq3-dev
pip install pyzmq

Note: for using the serial port, you need to change current user permissions:

# Add user to dialout group to have the right to write on the serial port
sudo usermod -a -G dialout $USER
# You need to logout/login again for that change to be taken into account

Additional python dev-dependencies for training the neural network: On your laptop:

pip install pytorch
pip install torchvision

On the raspberry pi :

  • You can try to use Python 2 wheel (not tested) that was created for this project:
  1. Download Python Wheel here

And then:

pip install torch-0.4.0a0+b23fa21-cp27-cp27mu-linux_armv7l.whl

Or follow this tutorial: PyTorch on the Raspberry Pi

  1. Make sure you have at least 3 Go of Swap. (see link above)
sudo dd if=/dev/zero of=/swap1 bs=1M count=3072 Status=progress
sudo mkswap /swap1
sudo swapon /swap1
  1. (optional) Install a recent version of cmake + scikit-build + ninja

  2. Install PyTorch

See for dependencies. Additional dependencies:

sudo apt-get install libopenblas-dev libeigen3-dev libffi-dev
# don't forget to set the env variables:
export NO_CUDA=1
git clone --recursive
python install --user
# torchvision is not used yet
pip install torchvision --user

Wifi on RPI

OpenCV with Python 2 (RPI), compiling from source (cf Docs and tuto): Additional dependencies:

sudo apt-get install libtbb-dev opencl-headers libomp-dev libopenblas-dev libeigen3-dev
sudo apt-get install libatlas-base-dev gfortran -y

with Gstreamer Then:

cmake -D CMAKE_INSTALL_PREFIX=/usr/local \
-D BUILD_opencv_java=OFF \
-D BUILD_opencv_python2=ON \
-D BUILD_opencv_python3=OFF \

OpenCV with Anaconda, compiling from source:

cmake -DPYTHON_EXECUTABLE=/home/ỳour_name/anaconda3/bin/python3 \
-DPYTHON_INCLUDE=/home/ỳour_name/anaconda3/include \
-DPYTHON_LIBRARY=/home/ỳour_name/anaconda3/lib/ \
-DPYTHON_PACKAGES_PATH=/home/ỳour_name/anaconda3/lib/python3.6/site-packages \

Docker Support (Experimental)


Build docker image (laptop image):

docker build . -f docker/Dockerfile.cpu -t racing-robot-cpu

Build docker image (raspberry pi image):

docker build . -f docker/DockerfileBase.rpi3 -t racing-robot-rpi3-base
docker build . -f docker/Dockerfile.rpi3 -t racing-robot-rpi3


  • Sergio Nicolas Rodriguez Rodriguez
  • Antonin Raffin