An optimal solver for Multi-Agent Path Finding.

This solver consists of Conflict-Based Search [1] and many of its improvement techniques, including

• Prioritizing conflicts [2]
• Bypassing conflicts [3]
• High-level admissible heuristics:
  • CG [4]
  • DG [5]
  • WDG [5]
• Symmetry reasoning techniques:
  • rectangle reasoning [6] and generalized rectangle reasoning [7]
  • target reasoning [8]
  • corridor reasoning [8] and corridor-target reasoning [7]
  • mutex propagation [9]
• Disjoint splitting [10]

Please cite the following paper if you use the code in your published research:
Jiaoyang Li, Daniel Harabor, Peter J. Stuckey, Hang Ma, Graeme Gange and Sven Koenig. Pairwise Symmetry Reasoning for Multi-Agent Path Finding Search. Artifical Intelligence (AIJ), volume 301, pages 103574, 2021.

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:

./cbs -m random-32-32-20.map -a random-32-32-20-random-1.scen -o test.csv --outputPaths=paths.txt -k 30 -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: runtime limit (in seconds)

The above command runs the best variant of the code reported in [7] (i.e., using prioritizing conflicts, bypassing conflicts, WDG heuristics, target reasoning, and generalized rectangle and corridor reasoning).

If you want to turn on/off some techniques, you can find more details and explanations for all parameters with:

./cbs --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.

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

I would like to thank Han Zhang for providing the code for mutex propagation.

[1] Guni Sharon, Roni Stern, Ariel Felner, and Nathan R. Sturtevant. Conflict-Based Search for Optimal Multi-Agent Pathfinding. Artificial Intelligence, 219:40–66, 2015.

[2] Eli Boyarski, Ariel Felner, Roni Stern, Guni Sharon, David Tolpin, Oded Betzalel, and Solomon Eyal Shimony. ICBS: Improved Conflict-Based Search Algorithm for Multi-Agent Pathfinding. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pages 740–746, 2015.

[3] Eli Boyarski, Ariel Felner, Guni Sharon, and Roni Stern. Don't Split, Try to Work It Out: Bypassing Conflicts in Multi-Agent Pathfinding. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), pages 47-51, 2015.

[4] Ariel Felner, Jiaoyang Li, Eli Boyarski, Hang Ma, Liron Cohen, T. K. Satish Kumar, and Sven Koenig. Adding Heuristics to Conflict-Based Search for Multi-Agent Path Finding. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), pages 83-87, 2018.

[5] Jiaoyang Li, Ariel Felner, Eli Boyarski, Hang Ma, and Sven Koenig. Improved Heuristics for Multi-Agent Path Finding with Conflict-Based Search. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pages 442-449, 2019.

[6] Jiaoyang Li, Daniel Harabor, Peter J. Stuckey, Hang Ma, and Sven Koenig. Symmetry-Breaking Constraints for Grid-Based Multi-Agent Path Finding. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pages 6087-6095, 2019.

[7] Jiaoyang Li, Daniel Harabor, Peter J. Stuckey, and Sven Koenig. Pairwise Symmetry Reasoning for Multi-Agent Path Finding Search. CoRR, abs/2103.07116, 2021.

[8] Jiaoyang Li, Graeme Gange, Daniel Harabor, Peter J. Stuckey, Hang Ma, and Sven Koenig. New Techniques for Pairwise Symmetry Breaking in Multi-Agent Path Finding. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), pages 193-201, 2020.

[9] Han Zhang, Jiaoyang Li, Pavel Surynek, Sven Koenig, and T. K. Satish Kumar. Multi-Agent Path Finding with Mutex Propagation. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), pages 323-332, 2020.

[10] Jiaoyang Li, Daniel Harabor, Peter J. Stuckey, Ariel Felner, Hang Ma, and Sven Koenig. Disjoint Splitting for Multi-Agent Path Finding with Conflict-Based Search. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), pages 279-283, 2019.