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Fix docs
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AgarwalSaurav committed Mar 19, 2024
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11 changes: 10 additions & 1 deletion 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_.
The library provides a simulation environment, algorithms, and GNN-based architectures for the coverage control problem.
<img align="right" width="300" src="https://github.com/KumarRobotics/CoverageControl/blob/main/doc/graphics/LPAC.gif">
<img align="right" width="300" src="https://kumarrobotics.github.io/CoverageControl/LPAC.gif">

**Key features:**
- The core library `CoverageControlCore` is written in `C++` and `CUDA` to handle large-scale simulations
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- 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:
```bash
pip install coverage_control
```

See the [Quick Start](https://kumarrobotics.github.io/CoverageControl/quick_start.html) guide for a quick introduction to the library.

---

## Citation
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10 changes: 10 additions & 0 deletions doc/manual/README.md
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---

## Quick Start
The library is available as a `pip` package. To install the package, run the following command:
```bash
pip install coverage_control
```

See the [Quick Start](quick_start.md) guide for a quick introduction to the library.

---

## Citation
```
@article{agarwal2024lpac,
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2 changes: 1 addition & 1 deletion doc/manual/lpac.md
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\page lpac LPAC Neural Network
\tableofcontents

# Prelminaries
# Preliminaries
We will organize files in a **workspace** directory: `${CoverageControl_ws}` (e.g., ~/CoverageControl\_ws).

Download and extract the file `lpac_CoverageControl.tar.gz` to the workspace directory.
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