sznie / pbs

PBS

A suboptimal solver for Multi-Agent Path Finding

Priority-Based Search (PBS) is an efficient suboptimal algorithm for solving Multi-Agent Path Finding (MAPF). More details can be found in our paper at AAAI 2019 [1]. (This implementation is not the original code for producing the results in the paper.)

The implementation provides a SIPP option that uses SIPPS [2] (instead of state-time A*) in the low level of PBS to plan paths for agents.

Usage

The code requires the external library boost. If you are using Ubantu, you can install it simply by

sudo apt install libboost-all-dev

Another easy way of installing the boost library is to install anaconda/miniconda and then

conda install -c anaconda libboost

which works for a variety of systems (including linux, osx, and win).

If neither of the above method works, you can also follow the instructions on the boost website and install it manually.

After you installed boost 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:

./pbs -m random-32-32-20.map -a random-32-32-20-random-1.scen -o test.csv --outputPaths=paths.txt -k 50 -t 60

• m: the map file from the MAPF benchmark
• a: the scenario file from the MAPF benchmark
• o: the output file that contains the search statistics
• outputPaths: the output file that contains the paths
• k: the number of agents
• t: the runtime limit

You can find more details and explanations for all parameters with:

./pbs --help

To test the code on more instances, you can download the MAPF instances from the MAPF benchmark. In particular, the format of the scen files is explained here. 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.

License

PBS is released under USC – Research License. See license.md for further details.

References

[1] Hang Ma, Daniel Harabor, Peter J. Stuckey, Jiaoyahng Li and S. Koenig. Searching with Consistent Prioritization for Multi-Agent Path Finding. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 7643-7650, 2019.

[2] 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.