h2jaafar / pb-cnpp

A repo containing scripts for preparing PathBench json maps for compatibility with CNPP algorithm.

pb-cnpp

A repo containing scripts for preparing PathBench json maps for compatibility with CNPP algorithm.

Acknowledgments

The original source code for this repo is from Path Planning Using Deep Learning.

The paper, One-shot path planning for multi-agent systems using fully convolutional neural network, presents the algorithm in more detail.

What is changed

The purpose of this repo is to compare WPN to CNPP. To achieve this, I had to train and test using PathBench generated maps. The maps are transformed to a compatible format in json_to_dat.py. This is the major addition.

I also implemented some basic metrics, such as success rates, deviation, prediction times, and distance left when failed.

There is a resources folder that is omitted from the repo due to the size. I have a shortened version in as sample_resources, which has the basic file structure that is required as inputs. lengths_house.json are the path lengths exported from PathBench. Similarly, paths_house.json are the Astar paths exported from PathBench. These are used for ground-truth, as well as deviation metrics.

Dat files:

The CNPP code takes dat files as inputs. Each row of the dat file is a map, with length of n x n, where n is the size of the map. i.e, a 8x8 map would have a row-length of 64. Obstacles are denoted by 1.

g_maps.dat is the position of the goal, and s_maps.dat is the position of the start point. inputs.dat is the obstacle map, and outputs.dat is the astar paths.

Trained Model

The model that is trained on PathBench maps is model_2d_30k_combined_2.hf5. This was trained on 30,000 64x64 maps.

Inference

To infer/test on maps, you can use any one of the predict_path... files. The differences were simply for ease of tracking, as I had different experiments setup for WPN comparisions.

Future works

There are no plans to continue/add on to this implementation from my end, however, feel free to submit a PR with any changes you think are suited.