lbr_stack_doc

This repository holds the documentation for the LBR-Stack.

Build Documentation Locally

To build locally:

1. Clone this repository

   git clone --recursive [email protected]:lbr-stack/lbr_stack_doc.git
   cd lbr_stack_doc

2. Create a virtual environment

   python3 -m venv ./lbr_stack_doc_venv
   source lbr_stack_doc_venv/bin/activate

3. Install dependencies

   pip3 install -r requirements.txt

4. Clone the lbr_fri_ros2_stack, e.g. via (this uses vcs)

   wget https://raw.githubusercontent.com/lbr-stack/lbr_fri_ros2_stack/humble/lbr_fri_ros2_stack/repos-fri-1.15.yaml
   vcs import doc/source < repos-fri-1.15.yaml

5. Clone pyfri

   git clone https://github.com/lbr-stack/pyfri.git doc/source/pyfri

6. In conf.py change

   f"doxysphinx build . $READTHEDOCS_OUTPUT/html {doxyfile}", shell=True

   to

   f"doxysphinx build . html {doxyfile}", shell=True

7. Finally, go to doc/source and run

   python3 -m sphinx -T -E -b html -d _build/doctrees -D language=en . html

8. Open and browse the documentation by opening doc/source/html/index.html.

Build Paper Locally

To build the paper via Docker, run

docker run --rm -it \
    -v $PWD:/data \
    -u $(id -u):$(id -g) \
    openjournals/inara \
    -o pdf,crossref,preprint \
    paper/paper.md

inside the lbr_stack_doc repository.

Citation

If you enjoyed using this repository for your work, we would really appreciate ❤️ if you could leave a ⭐ and / or cite it, as it helps us to continue offering support.

@misc{huber2023lbrstack,
      title={LBR-Stack: ROS 2 and Python Integration of KUKA FRI for Med and IIWA Robots}, 
      author={Martin Huber and Christopher E. Mower and Sebastien Ourselin and Tom Vercauteren and Christos Bergeles},
      year={2023},
      eprint={2311.12709},
      archivePrefix={arXiv},
      primaryClass={cs.RO}
}

Acknowledgements

This work was supported by core and project funding from the Wellcome/EPSRC [WT203148/Z/16/Z; NS/A000049/1; WT101957; NS/A000027/1].

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101016985 (FAROS project).