# pibt2
**Repository Path**: futurelei/pibt2
## Basic Information
- **Project Name**: pibt2
- **Description**: 实时mapf规划算法，用于长期运行的AGV调度系统动态规划问题
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: public
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-06-22
- **Last Updated**: 2026-06-22
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
pibt2
===
[](LICENSE)
The code repository of the paper "[Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding](https://kei18.github.io/pibt2/)" (AIJ-22; the journal version).
Updated
- 2023-10-02: The visualizer for Ubuntu is available [here](https://github.com/kei18/mapf-visualizer).
- 2023-07-26: [A minimal Python implementation](https://github.com/Kei18/pypibt) is now available.
Description
- There is [an old implementation](https://github.com/Kei18/pibt) for [the conference paper (IJCAI-19)](https://www.ijcai.org/proceedings/2019/76); this repo is much faster and cleaner.
- The implementation includes both Multi-Agent Path Finding (MAPF) and Multi-Agent Pickup and Delivery (MAPD).
- It is written in C++(17) with [CMake](https://cmake.org/) (≥v3.16) build.
- The repository uses [Google Test](https://github.com/google/googletest) and [the original library for 2D pathfinding](https://github.com/Kei18/grid-pathfinding) as git submodules.
- The visualizer uses [openFrameworks](https://openframeworks.cc) and works only on macOS (as git submodule).
- The accompanied solvers are HCA\* [1], Push and Swap [2], TP [3], and PIBT(+).
| platform | status (public) | status (dev) |
| ---: | :--- |:--- |
| macos-10.15 |   |   |
| ubuntu-latest |  |  |
Please cite the following paper if you use the code in your published research:
```
# AIJ-22
@article{okumura2022priority,
title = {Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding},
journal = {Artificial Intelligence},
pages = {103752},
year = {2022},
issn = {0004-3702},
doi = {https://doi.org/10.1016/j.artint.2022.103752},
author = {Keisuke Okumura and Manao Machida and Xavier Défago and Yasumasa Tamura},
}
# IJCAI-19
@inproceedings{okumura2019priority,
title={Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding},
author={Okumura, Keisuke and Machida, Manao and D{\'e}fago, Xavier and Tamura, Yasumasa},
booktitle={Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, {IJCAI-19}},
publisher={International Joint Conferences on Artificial Intelligence Organization},
pages={535--542},
year={2019},
month={7},
doi={10.24963/ijcai.2019/76},
url={https://doi.org/10.24963/ijcai.2019/76}
}
```
## Demo
## Building
```sh
git clone --recursive https://github.com/Kei18/pibt2.git
cd pibt2
mkdir build && cd build
cmake ..
make
```
### Docker
You can also use the docker environment instead of the native one, based on Ubuntu18.04.
```sh
docker compose up -d
docker compose exec dev bash
```
## Usage
### MAPF
PIBT
```sh
./mapf -i ../instances/mapf/sample.txt -s PIBT -o result.txt -v
```
You can find details and explanations for all parameters with:
```sh
./mapf --help
```
Please see `instances/mapf/sample.txt` for parameters of instances, e.g., filed, number of agents, time limit, etc.
Output File
This is an example output of `../instances/mapf/sample.txt`.
Note that `(x, y)` denotes location.
`(0, 0)` is the left-top point.
`(x, 0)` is the location at `x`-th column and 1st row.
```
instance=../instances/mapf/sample.txt
agents=100
map_file=arena.map
solver=PIBT
solved=1
soc=3738
lb_soc=3243
makespan=68
lb_makespan=68
comp_time=11
starts=(32,21),(40,4),(20,22),(26,18), [...]
goals=(10,16),(30,21),(11,42),(44,6), [...]
solution=
0:(32,21),(40,4),(20,22),(26,18), [...]
1:(31,21),(40,5),(20,23),(27,18), [...]
[...]
```
Output File
This is an example output of `../instances/mapd/sample.txt`.
- task: {task-id}:{loc-pickup}->{loc-delivery},appear={timestep},finished={timestep}
- solution: {timestep}:(loc-current)->(loc-current-target):{assigned-task-id or -1}
```
instance=../instances/mapd/sample.txt
agents=50
map_file=warehouse.map
solver=PIBT
solved=1
service_time=25.33
makespan=535
comp_time=437
preprocessing_comp_time0
starts=(33,17),(32,19), [...]
task=
0:129->113,appear=0,finished=23
4:342->256,appear=4,finished=24
[...]
solution=
0:(33,17)->(24,3):-1,(32,19)->(24,3):-1, [...]
1:(32,17)->(29,2):-1,(31,19)->(29,2):-1, [...]
[...]
```