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Fault-Tolerant Neural CBF

FTNCBF 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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<h2 align="center">Fault Tolerant Neural Control Barrier Functions</h2>

Fault Tolerant Neural Control Barrier Functions for Robotic Systems under Sensor Faults and Attacks (ICRA 2024)
 
 <a href="https://github.com/HongchaoZhang-HZ/FTNCBF/blob/main/ICRA2024_FTNCBF_CameraReady.pdf">Full Paper »</a>
 
 
</div>

 <img src="https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54" alt="Python">
 <img src="https://img.shields.io/badge/PyTorch-%23EE4C2C.svg?style=for-the-badge&logo=PyTorch&logoColor=white" alt="PyTorch">

<summary>Table of Contents</summary>
 <ol>
 
 <li><a href="#experiments">Experiments</a></li>
 <li>
 <a href="#getting-started">Getting Started</a>
 <ul>
 <li><a href="#installation">Installation</a></li>
 <li><a href="#run-the-code">Run the Code</a></li>
 </ul>
 </li>
 <li><a href="#citation"> Citation</a></li>
 
 <li><a href="#license">License</a></li>
 <li><a href="#contact">Contact</a></li>
 <li><a href="#acknowledgments">Acknowledgments</a></li>
 </ol>

<img src="./FTNCBF_comp.gif" alt="gif" width="80%">
 </a>

## Experiments

**Obstacle Avoidance:** We evaluate our proposed method on a controlled system [[1]](barry2). We consider an Unmanned Aerial Vehicle (UAV) to avoid collision with a tree trunk. We model the system as a  Dubins-style [[2]](dubins1957curves) aircraft model. The system state consists of a 2D position and aircraft yaw rate $x:=[x_1, x_2, \psi]^T$. We let $u$ denote the control input to manipulate the yaw rate and the dynamics defined in the supplement. 
We train the NCBF via the method proposed in [[3]](zhao2020synthesizing) with $v$ assumed to be $1$ and the control law $u$ designed as
 $u=\mu_{nom}(x)=-\sin \psi+3 \cdot \frac{x_1 \cdot \sin \psi+x_2 \cdot \cos \psi}{0.5+x_1^2+x_2^2}$.

**Spacecraft Rendezvous:** We evaluate our approach to a spacecraft rendezvous problem from [[5]](jewison2016spacecraft). A station-keeping controller is required to keep the "chaser" satellite within a certain relative distance from the "target" satellite. The state of the chaser is expressed relative to the target using linearized Clohessy–Wiltshire–Hill equations, with state $x=[p_x, p_y, p_z, v_x, v_y, v_z]^T$, control input $u=[u_x, u_y, u_z]^T$ and dynamics defined in the supplement. We train the NCBF as in [[6]](dawson2023safe).

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## Getting Started

This is an example of how you may give instructions on setting up your project locally.
To get a local copy up and running follow these simple example steps.

### Installation

Clone the repo and navigate to the folder
```sh
git clone https://github.com/HongchaoZhang-HZ/FTNCBF.git

cd FTNCBF
```

Install packages via pip
   ```sh
   pip install -r requirements.txt
   ```

### Run the code

Choose the system and corresponding NCBFs, e.g., train NCBF for vehicle obstacle avoidance, to train by running the code
   ```sh
   python main_Obs.py
   ```

### Run Obstacle Avoidance in Carla
Copy code and the trained NCBF to Carla folder `PythonAPI/examples`, and run 
   ```sh
   python main.py
   ```

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## Citation
If our work is useful for your research, please consider citing:

```
@INPROCEEDINGS{zhang2024fault,
 author={Zhang, Hongchao and Niu, Luyao and Clark, Andrew and Poovendran, Radha},
 booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)}, 
 title={Fault Tolerant Neural Control Barrier Functions for Robotic Systems under Sensor Faults and Attacks}, 
 year={2024},
 volume={},
 number={}}
```

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## License

Distributed under the MIT License. See `LICENSE.txt` for more information.

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## Contact

If you have any questions, please feel free to reach out to us.

Hongchao Zhang - [Homepage](https://hongchaozhang-hz.github.io/) - hongchao@wustl.edu

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## Acknowledgments
This research was supported by the AFOSR (grants FA9550-22-1-0054 and FA9550-23-1-0208), and NSF (grants CNS-1941670).

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[zeng2016darboux]: https://dl.acm.org/doi/abs/10.1145/2968478.2968491
[barry2012safety]: https://ieeexplore.ieee.org/abstract/document/6224645
[dubiins1957curves]: https://www.jstor.org/stable/2372537?seq=1
[jewison2016spacecraft]: https://ieeexplore.ieee.org/abstract/document/7798763
[dawson2023safe]: https://ieeexplore.ieee.org/abstract/document/9531393
[abate2021fossil]: https://link.springer.com/chapter/10.1007/978-3-030-81608-0_4
[zhao2020synthesizing]: https://dl.acm.org/doi/abs/10.1145/3365365.3382222

FTNCBF

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