cltl.backend-naoqi 0.0.dev6

Backend for Pepper robot

cltl-backend-naoqi

NaoQI Backend for the Pepper Robot.

Functionality

This repository provides a Docker container that runs an web server that supports connections of the client sources provided in the cltl.backend.source.client_source module of the Leolani Backend.

The docker image provides endpoints for Audio, Images and connects to the robot through TCP to retrieve and send signals.

Audio

The container exposes an endpoint at /audio (GET) that provides an audio stream. The format of the stream is indicated by the mine type indicated in the response. Currently, this will be chunked audio data with mime type audio/L16, i.e. raw 16bit audio. In the mime type header, additional parameters for rate, channels and frame_size are provided.

Images

The container exposes an endpoint at /image (GET) that provides an image captured at invocation time. The endpoint returns a JSON object corresponding to cltl.backend.api.camera.Image that contains the raw image data, the image dimensions and depth information. Image dimensions are also inlcude in the mime type header of the response.

Build the backend Docker container

To build the docker image run

make build

This will create a docker image with tag cltl-backend-naoqi. To verify the build was successful run

make test

Run the backend on the robot

WIP

To run the backend directly on the robot we need to install the cltl.backend-naoqi package with its dependencies on the robot, make sure the ports of the web server are open and ensure the backend is started when the robot starts.

Package installation

On pepper a rather old Python version is installed, which causes issues with pip. Also, root access of the nao user is restricted. pip can still be upgraded for the user using (run in a separate directory)

wget https://files.pythonhosted.org/packages/ca/1e/d91d7aae44d00cd5001957a1473e4e4b7d1d0f072d1af7c34b5899c9ccdf/pip-20.3.3.tar.gz
pip install --user pip-20.3.3.tar.gz

Since we do not want to interfere with the Python environment on pepper we install the package in a virutal environment. For the installation the scripts/install_libs.sh is provided. Copy it to the robot and execute it. This will create a virtual environment, download the necessary packages and install them.

Running the backend

To run the backend we need to find an unused port that can be used by the web server and make sure the firewall on Pepper allows access it.

Once the package is installed it can be run with

source venv/bin/activate
python -m cltl.naoqi --naoqi-ip 127.0.0.1

This is also provided in the scripts/run_backend_naoqi.sh script.

Run at startup

NaoQi startup is described here. We need to figure out a way run the backend at startup.

Run the backend Docker container

At the current moment the instructions below only work on LINUX, as the --network host option for Docker is only available on LINUX and some services registered with the qi framework are not visible to the ALAudioDevice Module when we run inside the Docker container with a bridge network.

To run the docker image use:

docker run --rm -it -e CLTL_NAOQI_IP="192.0.0.1" --network host cltl/cltl-backend-naoqi

It is mandatory to provide the IP of the Pepper robot in the CLTL_NAOQI_IP environment variable. Further configurations can be set through environment variables, for a list run

docker run --rm -it cltl/cltl-backend-naoqi python -m cltl.naoqi --help

Test if the backend is working

WIP

To test if the backend is working we can curl the REST API of the backend

curl <backend ip>/video
curl <backend ip>/audio
curl --data "Hallo Stranger!" <backend ip>/text

Unit test

The unit tests in this repository require the NAOqi SDK and Python 2.7 to be installed. They are, however, included in the Docker image provided by this repository and can be run with:

make build
docker run --rm -it cltl/cltl-backend-naoqi python -m unittest discover

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Authors

• Taewoon Kim
• Thomas Baier
• Selene Báez Santamaría
• Piek Vossen