A repo containing scripts for preparing PathBench json maps for compatibility with CNPP algorithm.
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.
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.
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.
The model that is trained on PathBench maps is model_2d_30k_combined_2.hf5. This was trained on 30,000 64x64 maps.
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.
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.