https://github.com/srujan-d/ak_catnipp

modifying CAtNIPP to work with AK
https://github.com/srujan-d/ak_catnipp

Last synced: about 21 hours ago
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modifying CAtNIPP to work with AK

• Host: GitHub
• URL: https://github.com/srujan-d/ak_catnipp
• Owner: Srujan-D
• License: mit
• Created: 2024-02-09T22:44:57.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-02-12T22:18:17.000Z (8 months ago)
• Last Synced: 2024-10-02T18:27:40.534Z (about 21 hours ago)
• Language: Python
• Size: 23.3 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# CAtNIPP: Context-Aware Attention-based Network for Informative Path Planning

A context-aware neural framework for adaptive informative path planning (IPP) problem.

## Run

#### Training

1. Install requirements at the bottom.

2. Set appropriate parameters in `parameters.py`, including `NUM_META_AGENT`, `CUDA_DEVICE`, `BATCH_SIZE` (recommand 256 for every 8GB VRAM).

3. Name your run with `FOLDER_NAME`.

4. Run `python driver.py`

#### Evaluation

1. Set appropriate parameters in `/eval/test_parameters.py`, including `FOLDER_NAME`, `NUM_TEST`, `TRAJECTORY_SAMPLING`, `SAVE_IMG_GAP`, etc.

2. Run `/eval/test_driver.py`

## Files

* `parameters.py` Training parameters.

* `driver.py` Driver of training program, maintain & update the global network.

* `runner.py` Wrapper of the local network.

* `worker.py` Interact with environment and collect episode experience.

* `attention_net.py` Define context-aware attention-based network.

* `env.py` Informative path planning environment.

* `gp_ipp.py` Gaussian Process and metrics calculation.

* `/eval` Test files for evaluation, similar to training.

* `/classes` Utilities for generating graph, ground truth, etc.

* `/model` Trained model.

### Demo of trajectory sampling variant CAtNIPP

![ts_demo](./result/ts_demo.gif)

### Requirements

```

python>=3.6

numpy>=1.17

ray>=1.15  % Ray should match python version

pytorch>=1.7

scipy

scikit-learn

matplotlib

imageio

shapely

```

### Cite

```

@InProceedings{cao2022catnipp,

  title = {Context-Aware Attention-based Network for Informative Path Planning},

  author = {Cao, Yuhong and Wang, Yizhuo and Vashisth, Apoorva and Fan, Haolin and Sartoretti, Guillaume},

  booktitle = {6th Annual Conference on Robot Learning},

  year = {2022}

}

```

### Authors

[Yuhong Cao](https://github.com/caoyuhong001)\

[Yizhuo Wang](https://github.com/wyzh98)\

[Apoorva Vashisth](https://github.com/AccGen99)\

[Haolin Fan](https://github.com/FHL1998)\

[Guillaume Sartoretti](https://github.com/gsartoretti)