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Saurav Agarwal
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Update setup and doc
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.github/workflows/cd.yml

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FORCE_COLOR: 3
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jobs:
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make_sdist:
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name: Make SDist
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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with:
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fetch-depth: 0
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- name: Install dependencies
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run: sudo bash ./setup_utils/install_dependencies.sh --boost --gmp --mpfr --eigen --cgal
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- name: Build SDist
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run: pipx run build --sdist
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- uses: actions/upload-artifact@v4
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with:
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name: cibw-sdist
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path: dist/*.tar.gz
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build_wheels:
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name: Wheel on ${{ matrix.os }}
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runs-on: ${{ matrix.os }}
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strategy:
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fail-fast: false
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matrix:
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os: [ubuntu-latest]
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steps:
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- uses: actions/checkout@v4
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with:
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fetch-depth: 0
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- uses: pypa/[email protected]
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- name: Upload wheels
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uses: actions/upload-artifact@v4
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with:
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name: cibw-wheels-${{ matrix.os }}-${{ strategy.job-index }}
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path: wheelhouse/*.whl
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upload_all:
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needs: [build_wheels, make_sdist]
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environment:
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name: pypi
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url: https://pypi.org/p/coverage-control
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permissions:
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id-token: write
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runs-on: ubuntu-latest
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if: github.event_name == 'release' && github.event.action == 'published'
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steps:
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- uses: actions/download-artifact@v4
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with:
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pattern: cibw-*
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path: dist
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merge-multiple: true
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- uses: pypa/gh-action-pypi-publish@release/v1
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# with:
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# Remember to tell (test-)pypi about this repo before publishing
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# Remove this line to publish to PyPI
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# repository-url: https://test.pypi.org/legacy/
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docker-deploy:
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permissions: write-all
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runs-on: ubuntu-latest

.github/workflows/cd_wheels.yml

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.github/workflows/ghaction-doxygen-ghpages.yml

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workflow_dispatch:
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release:
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types: [published]
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push:
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paths:
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- 'doc/**'
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permissions:
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contents: write
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README.md

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Coverage control is the problem of navigating a robot swarm to collaboratively monitor features or a phenomenon of interest not known _a priori_.
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The library provides a simulation environment, algorithms, and GNN-based architectures for the coverage control problem.
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<img align="right" width="300" src="https://github.com/KumarRobotics/CoverageControl/raw/main/doc/graphics/LPAC.gif">
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<img align="right" width="300" src="https://kumarrobotics.github.io/CoverageControl/LPAC.gif">
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**Key features:**
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- The core library is written in `C++` and `CUDA` to handle large-scale simulations
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- There are `python` bindings that interface with the core library
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- Several Centroidal Voronoi Tessellation (CVT)-based algorithms (aka Lloyd's algorithms)
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- Learnable Perception-Action-Communication (LPAC) architecture for the coverage control problem is implemented in `PyTorch` and `PyTorch Geometric`
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- GPU and CPU parallelization using `CUDA` and `OpenMP`
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---
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## Quick Start
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## Getting Started
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The library is available as a `pip` package. To install the package, run the following command:
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```bash
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pip install coverage_control

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