Fault Tolerant Neural Control Barrier Functions

Fault Tolerant Neural Control Barrier Functions for Robotic Systems under Sensor Faults and Attacks (ICRA 2024)
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Table of Contents

1. Experiments
2. Getting Started
   • Installation
   • Run the Code
3. Citation
4. License
5. Contact
6. Acknowledgments

Experiments

Obstacle Avoidance: We evaluate our proposed method on a controlled system [1]. We consider an Unmanned Aerial Vehicle (UAV) to avoid collision with a tree trunk. We model the system as a Dubins-style [2] 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] 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]. 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].

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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

git clone https://github.com/HongchaoZhang-HZ/FTNCBF.git

cd FTNCBF

Install packages via pip

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

python main_Obs.py

Run Obstacle Avoidance in Carla

Copy code and the trained NCBF to Carla folder PythonAPI/examples, and run

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 - [email protected]

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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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