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An Efficient Multi-Agent Path Finding Solver for Car-Like Robots
| Date | Stars |
|---|---|
| 2026-07-31 | 413 |
| 2026-08-06 | 414 |
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# CL-CBS
## Overview
**Car-Like Conflict-Based Search (CL-CBS)** is an efficient and complete solver of Multi-Agent Path Finding for Car-like Robots problem. It applies a body conflict tree to address collisions considering the shape of agents. It also includes a new algorithm Spatiotemporal Hybrid-State A* as the single-agent path planner to generate path satisfying both kinematic and spatiotemporal constraints.
<img src="img/8car.gif" width="60%" height="60%">
The video demonstration can be found on [YouTube](https://www.youtube.com/watch?v=KThsX04ABvc)
## Source Code
### Requirement
```bash
sudo apt-get install g++ cmake libboost-program-options-dev libyaml-cpp-dev \
clang-tidy clang-format python3-matplotlib libompl-dev libeigen3-dev
```
> Note: Please make sure your `matplotlib` version is above `2.0`, otherwise it may show weird image while visualization. You can upgrade it by `pip3 install -U matplotlib`.
### Build
```bash
mkdir build
cd build
cmake -DCMAKE_BUILD_TYPE=Release ..
make -j8
```
* `make`: Build CL-CBS code
* `make docs`: Build doxygen documentation
* `make clang-format`: Re-format all source files
* `make all`: Build all three targets above
### Run example instances
```bash
# make sure your are in build folder
# default 10 agent in a batch
./CL-CBS -i ../benchmark/map100by100/agents20/obstacle/map_100by100_obst50_agents20_ex13.yaml -o output.yaml
# or compute 20 agents in a whole batch
./CL-CBS -i ../benchmark/map100by100/agents20/obstacle/map_100by100_obst50_agents20_ex13.yaml -o output.yaml -b 20
```
### Visualize Results
```bash
# make sure your are in build folder
python3 ../src/visualize.py -m ../benchmark/map100by100/agents20/obstacle/map_100by100_obst50_agents20_ex13.yaml -s output.yaml
```
### Agent Configuration
The agent configurations, including the size, the kinematic constraints, and penalty functions can be changed in `src/config.yaml`.
## Benchmark
Benchmark for evaluating CL-MAPF problem are available in `benchmark` folder. It contains 3000 unique instances with different map size and agents number.
The folder are arranged like follows, each mapset contains 60 instances:
```
benchmark
├── map100by100
│ ├── agents10
│ │ ├── empty
│ │ └── obstacle
│ ...
├── map300by300
│ ├── agents10
│ │ ├── empty
│ │ └── obstacle
│ ...
└── map50by50
├── agents10
│ ├── empty
│ └── obstacle
...
```
The instance are in `yaml` format.
A typical result from benchmark acts like below:
<img src="img/dataset.gif" width="60%" height="60%">
## Credits
For researchers that have leveraged or compared to this work, please cite the following:
```
@article{WEN2022103997,
title = {CL-MAPF: Multi-Agent Path Finding for Car-Like robots with kinematic and spatiotemporal constraints},
journal = {Robotics and Autonomous Systems},
volume = {150},
pages = {103997},
year = {2022},
issn = {0921-8890},
doi = {https://doi.org/10.1016/j.robot.2021.103997},
url = {https://www.sciencedirect.com/science/article/pii/S0921889021002530},
author = {Licheng Wen and Yong Liu and Hongliang Li},
}
```
## License
The code was developed by the [APRIL Lab](https://github.com/APRIL-ZJU) in Zhejiang University, and is provided under the [MIT License](https://opensource.org/licenses/MIT).
Excerpt of 3,436 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:967b907ebb02a43d, llm:Repository topics: cbs, mapf, multiagent-planning, planning, robots; description: 'An Efficient Multi-Agent Path Finding Solver for Car-Like Robots' (C++ implementation).
matched fp:967b907ebb02a43d, llm:Repository topics: cbs, mapf, multiagent-planning, planning, robots; description: 'An Efficient Multi-Agent Path Finding Solver for Car-Like Robots' (C++ implementation).
matched fp:967b907ebb02a43d, llm:Repository topics: cbs, mapf, multiagent-planning, planning, robots; description: 'An Efficient Multi-Agent Path Finding Solver for Car-Like Robots' (C++ implementation).