MAPF-LNS2
MAPF-LNS2: Fast Repairing for Multi-Agent Path Finding via Large Neighborhood Search
MAPF-LNS2 is an efficient algorithm for solving Multi-Agent Path Finding (MAPF). More details can be found in our paper at AAAI 2022 [1].
The code requires the external libraries BOOST (https://www.boost.org/) and Eigen (https://eigen.tuxfamily.org/). An easy way to install the required libraries on Ubuntu:
sudo apt update- Install the Eigen library (used for linear algebra computing)
sudo apt install libeigen3-dev
- Install the boost library
sudo apt install libboost-all-dev
After you installed both libraries and downloaded the source code, go into the directory of the source code and compile it with CMake:
cmake -DCMAKE_BUILD_TYPE=RELEASE .
make
Then, you are able to run the code:
./lns -m random-32-32-20.map -a random-32-32-20-random-1.scen -o test.csv -k 400 -t 300
- m: the map file from the MAPF benchmark
- a: the scenario file from the MAPF benchmark
- o: the output file
- k: the number of agents
- t: the runtime limit
You can find more details and explanations for all parameters with:
./lns --help
We provide example instance files "random-32-32-20.map" and "random-32-32-20-random-1.scen" in the repo, More instances can be download from the MAPF benchmark (https://movingai.com/benchmarks/mapf/index.html). All the experiments in the paper used in instances from the benchmark except for Experiment 5, for which the instances are in folder "instances". In particular, the format of the scen files is explained here: https://movingai.com/benchmarks/formats.html. For a given number of agents k, the first k rows of the scen file are used to generate the k pairs of start and target locations.
Credits
The software was developed by Jiaoyang Li and Zhe Chen based on MAPF-LNS.
The rule-based MAPF solvers (i.e., PPS, PIBT, and winPIBT) inside the software were borrowed from https://github.com/Kei18/pibt/tree/v1.3
MAPF-LNS2 is released under USC – Research License. See license.txt for further details.
References
[1] Jiaoyang Li, Zhe Chen, Daniel Harabor, Peter J. Stuckey and Sven Koenig. MAPF-LNS2: Fast Repairing for Multi-Agent Path Finding via Large Neighborhood Search In Proceedings of the AAAI Conference on Artificial Intelligence, (in print), 2022.