A simulator and visualizer of Multi-Agent Path Finding (MAPF), used in a paper "Iterative Refinement for Realtime MAPF". It is written in C++(17) with CMake build and tested on OSX 10.15. The visualizer uses openFrameworks.

The implementations include: HCA* and WHCA* [1], PIBT [2], CBS [3], ICBS [4], ECBS [5], Revisit Prioritized Planning [6], Push and Swap [7], PIBT-Complete, and IR.

platform	status
macos-latest
ubuntu-latest

You can see the performance of each solver from auto_record repo. The records were created by Github Actions.

100 agents in arena, planned by PIBT in 67ms 7ms.

1000 agents in brc202d, planned by PIBT-Complete in 84sec 1683ms. The gif shows a part of an MAPF plan.

git clone --recursive https://github.com/Kei18/mapf-IR
cd mapf
mkdir build
cd build
cmake ..
make

PIBT

./app -i ../instances/sample.txt -s PIBT -o result.txt -v

IR (the result is saved in result.txt)

./app -i ../instances/random-32-32-20_70agents_1.txt -s IR -n 50 -t 3000 -v

You can find details and explanations for all parameters with:

./app --help

Please see instances/sample.txt for parameters of instances, e.g., filed, number of agents, time limit, etc.

This is an example output of ../instances/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/sample.txt
agents=100
map_file=arena.map
solver=PIBT
solved=1
soc=3403
makespan=68
comp_time=58
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), [...]
[...]

It takes around 10 minutes.

sh ./third_party/openFrameworks/scripts/osx/download_libs.sh
cd visualizer/
make build
cd ..
chmod +x ./visualize.sh

cd build
../visualize.sh result.txt

You can manipulate it via your keyboard. See printed info.

This software is released under the MIT License, see LICENSE.txt.

• Maps in maps/ are from MAPF benchmarks. When you add a new map, please place it in the maps/ directory.
• The font in visualizer/bin/data is from Google Fonts.
• Scripts for the experiments are in exp_scripts/.

Keisuke Okumura is a Ph.D. candidate at Tokyo Institute of Technology, working on multiple moving agents.

1. Silver, D. (2005). Cooperative pathfinding. AIIDE’05 Proc. AAAI Conf. on Artificial Intelligence and Interactive Digital Entertainment
2. Okumura, K., Machida, M., Défago, X., & Tamura, Y. (2019). Priority Inheritance with Backtracking for Iterative Multi-agent Path Finding. Proc. Intel. Joint Conf. on Artificial Intelligence (IJCAI)
3. Sharon, G., Stern, R., Felner, A., & Sturtevant, N. R. (2015). Conflict-based search for optimal multi-agent pathfinding. Artificial Intelligence
4. Boyarski, E., Felner, A., Stern, R., Sharon, G., Tolpin, D., Betzalel, O., & Shimony, E. (2015). ICBS: improved conflict-based search algorithm for multi-agent pathfinding. Proc. Intel. Joint Conf. on Artificial Intelligence (IJCAI)
5. Barer, M., Sharon, G., Stern, R., & Felner, A. (2014). Suboptimal Variants of the Conflict-Based Search Algorithm for the Multi-Agent Pathfinding Problem. Annual Symposium on Combinatorial Search (SoCS)
6. Čáp, M., Novák, P., Kleiner, A., & Selecký, M. (2015). Prioritized planning algorithms for trajectory coordination of multiple mobile robots. IEEE transactions on automation science and engineering
7. Luna, R., & Bekris, K. E. (2011). Push and swap: Fast cooperative path-finding with completeness guarantees. Proc. Intel. Joint Conf. on Artificial Intelligence (IJCAI)