Srujan-D/AK_CAtNIPP

modifying CAtNIPP to work with AK

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

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
Yizhuo Wang
Apoorva Vashisth
Haolin Fan
Guillaume Sartoretti