coverage-control 1.1.0

Library for large-scale coverage control using robot swarms

See full documentation at https://KumarRobotics.github.io/CoverageControl/

Introduction

Coverage control is the problem of navigating a robot swarm to collaboratively monitor features or a phenomenon of interest not known a priori. The library provides a simulation environment, algorithms, and GNN-based architectures for the coverage control problem.

Key features:

• The core library is written in C++ and CUDA to handle large-scale simulations
• There are python bindings that interface with the core library
• Several Centroidal Voronoi Tessellation (CVT)-based algorithms (aka Lloyd's algorithms)
• Learnable Perception-Action-Communication (LPAC) architecture for the coverage control problem is implemented in PyTorch and PyTorch Geometric
• GPU and CPU parallelization using CUDA and OpenMP

---

Quick Start

The library is available as a pip package. To install the package, run the following command:

pip install coverage_control

See Installation for more details on installation.

See Quick Start guide for a quick introduction to the library.

---

Citation

@article{agarwal2024lpac,
      title         =   {LPAC: Learnable Perception-Action-Communication Loops with
                            Applications to Coverage Control}, 
      author        =   {Saurav Agarwal and Ramya Muthukrishnan and 
                            Walker Gosrich and Vijay Kumar and Alejandro Ribeiro},
      year          =   {2024},
      eprint        =   {2401.04855},
      archivePrefix =   {arXiv},
      primaryClass  =   {cs.RO}
}

LPAC: Learnable Perception-Action-Communication Loops with Applications to Coverage Control.
Saurav Agarwal, Ramya Muthukrishnan, Walker Gosrich, Vijay Kumar, and Alejandro Ribeiro.
arXiv preprint arXiv:2401.04855 (2024).

Acknowledgements

• PyTorch
• PyTorch Geometric
• Eigen
• pybind11
• CGAL
• JSON for Modern C++
• CUDA Samples
• gnuplot-iostream
• hungarian-algorithm-cpp
• toml++

Support and Funding

The work was performed at the GRASP Laboratory and the Alelab, University of Pennsylvania, USA.

This work was supported in part by grants ARL DCIST CRA W911NF-17-2-0181 and ONR N00014-20-1-2822.

Contributors

• Saurav Agarwal
• Ramya Muthukrishnan

License

The library is licensed under the GPL-3.0 License. The documentation is not under the GPL-3.0 License and is licensed under the CC BY-NC-SA 4.0 License.