Stable-Time Path Planning

1. Installation

To reproduce our ablation results, please install Conda environment on a Linux machine with Nvidia GPU.
You may need to install the following apt packages:

sudo apt-get install libboost-dev

Please config the conda environment:

cd ~/DPtraj/deepPathPlan
conda create -n <your_env_name> python=3.8
conda activate <your_env_name>
pip install -r requirements.txt

2. Reproducing the Model

2.1 Download Training Data

Please download, unzip and place the Data as the directory ~/totalData. The completed dataset is suggested for reproducing the model as we found rich data makes performances better.

2.2 Training

You can easily retrain the model by running PathNet/train_ours.py:

cd ~/DPtraj/deepPathPlan
python PathNet/train_ours.py

2.3 Visualizing the Results

Once the model converges, you can visualize it:

cd ~/DPtraj/deepPathPlan
python PathNet/visualizer_tojit.py

Note: This script will utilize torch.jit.trace to generate a model file that can be directly invoked by LibTorch, allowing you to seamlessly integrate it into our ROS program.

3. Checkout our experiment logs

To check similar results in table Table.S1 and Fig.S2 of Supplementary Materials, we provide:

Detailed eval log model.pkl.txt.
Detailed training log model.pklStep.txt.
Reproduced model model.pkl.

Note:
Please note that slight differences in the results compared to the paper are normal, due to variations in training configuration and package versions.
For example, the batch size (bz) is set to 32 in this repo for easier reproduction on GPUs with 16 GB memory (the bz used in the paper is 64 based on 32 GB memory).

4. Contact

If you have any questions, please feel free to contact Zhichao HAN (zhichaohan@zju.edu.cn) or Mengze TIAN(mengze.tian@epfl.ch).

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