PyPI Installation

The library is available on PyPI and can be installed using pip. It is recommended to install the library inside a virtual environment.

pip install coverage_control

The package depends on the following packages

pip install torch torchvision torch-geometric

Note: PyTorch and PyTorch Geometric have CPU and CUDA-specific versions. The command installs the default version (latest CUDA).

We need the following optional packages for visualization and video generation:

gnuplot or gnuplot-nox (for visualizing environment)
ffmpeg (for generating videos)

On Ubuntu, these can be installed using the following command:

sudo apt install gnuplot-nox ffmpeg

Docker Installation

Docker images are available for the library with different configurations and versions.

Prerequisites (Optional)

We will organize files in a workspace directory: ${CoverageControl_ws} (e.g., ~/CoverageControl_ws). The workspace directory is mounted to the docker container.

Add the following lines to your ${HOME}/.bashrc file for convenience.

export CoverageControl_ws=${HOME}/CoverageControl_ws # Change to your workspace directory

Note: Close and reopen the terminal for the changes to take effect.

Clone the repository:

mkdir -p ${CoverageControl_ws}/src
git clone https://github.com/KumarRobotics/CoverageControl.git \
          ${CoverageControl_ws}/src/CoverageControl

Docker Container

Container can be created using the script in utils/docker/create_container.sh.

cd ${CoverageControl_ws}/src/CoverageControl/utils/docker

bash create_container.sh --with-cuda -d ${CoverageControl_ws} # See flags below

This will land you in a shell inside the container in the directory /workspace. If the workspace directory was specified, it will be mounted to the container at the same location.

One can exit the container by typing exit or pressing Ctrl+D.

The container can be started again using the following command:

docker start -i coverage-control-$USER # Replace with the name of the container

Flags:

-d <dir> : The workspace directory
-n <name>: Name of the container (default: coverage-control-$USER)
--with-cuda : With CUDA support
--with-ros : With ROS support
--noble : Ubuntu 24.04 Noble

The base image is agarwalsaurav/pytorch_base with different tags for different versions and configurations.

Tags Suffix	Flags
`jammy-torch2.5.1-cuda12.4.1-humble`	`--with-ros --with-cuda`
`jammy-torch2.5.1-cuda12.4.1`	`--with-cuda`
`jammy-torch2.5.1-humble`	`--with-ros`
`jammy-torch2.5.1`	None
`noble-torch2.5.1-cuda12.6.2-jazzy`	`--with-ros --with-cuda --noble`
`noble-torch2.5.1-cuda12.6.2`	`--with-cuda --noble`
`noble-torch2.5.1-jazzy`	`--with-ros --noble`
`noble-torch2.5.1`	`--noble`

Install the library available on PyPI:

pip install coverage_control

Alternatively, follow the Installation from Source instructions (except for the prerequisites).

Installation From Source

Prerequisites

The following packages are required to build the library:

sudo apt install libboost-all-dev libgmp-dev libmpfr-dev libeigen3-dev gnuplot-nox ffmpeg

Note: gnuplot-nox and ffmpeg are optional (but recommended) and only required for generating environment visualizations.

Additional dependencies (generally already installed):

sudo apt install build-essential cmake git wget python3 python3-pip python3-venv python3-dev

CUDA Support

(Optional but recommended for GPU acceleration)

The package also supports GPU acceleration using CUDA. To enable this feature, the following additional packages are required:

cmake (version 3.24 or higher)
cuda (version 11.8 or higher, 12.1 recommended)

Note: On Ubuntu, latest cmake version can be installed from the official Kitware APT Repository.

Automated Installation

pip install .

Building the Core C++ Library

Note: This is only necessary if you want to use the C++ library directly.

We will organize files in a workspace directory: ${CoverageControl_ws} (e.g., ~/CoverageControl_ws).

Add the following lines to your ~/.bashrc file.

export CoverageControl_ws=~/CoverageControl_ws # Change to your workspace directory
export PATH=${CoverageControl_ws}/install/bin:$PATH
export LD_LIBRARY_PATH=${CoverageControl_ws}/install/lib:$LD_LIBRARY_PATH

Clone the repository:

mkdir -p ${CoverageControl_ws}/src
git clone https://github.com/KumarRobotics/CoverageControl.git \
          ${CoverageControl_ws}/src/CoverageControl

The primary setup script is setup.sh located in the root of the repository.

cd ${CoverageControl_ws}/src/CoverageControl

bash setup.sh --with-deps -d ${CoverageControl_ws}

Testing the installation:

coverage_algorithm

There are multiple options for building the library.

Option	Description
`-d <dir>`	The workspace directory
`--with-cuda`	Build with CUDA support

Warning: Ubuntu 22.04 (Jammy) has CGAL 5.4 (libcgal-dev) in the official repositories, which has bugs and is not compatible with the library. The package requires CGAL 5.6, which is automatically installed from the official CGAL repository through CMake.

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