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.

The video demonstration can be found on YouTube

Source Code

Requirement

sudo apt-get install g++ cmake libboost-program-options-dev libyaml-cpp-dev clang-tidy clang-format python3-matplotlib libompl-dev libeigen3-dev

Build

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

# 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

# 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:

Credits

This code was developed by the APRIL Lab in Zhejiang University.

For researchers that have leveraged or compared to this work, please cite the following:

Licheng Wen, Zhen Zhang, Zhe Chen, Xiangrui Zhao, and Yong Liu. CL-MAPF: Multi-Agent Path Finding for Car-Like Robots with Kinematic and Spatiotemporal Constraints.[arxiv]

License

The code is provided under the MIT License.