https://github.com/mit-acl/mader
Trajectory Planner in Multi-Agent and Dynamic Environments
https://github.com/mit-acl/mader
mader minvo multi-agent obstacle-avoidance optimization path-planning planner ros trajectory-optimization
Last synced: 14 days ago
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Trajectory Planner in Multi-Agent and Dynamic Environments
- Host: GitHub
- URL: https://github.com/mit-acl/mader
- Owner: mit-acl
- License: bsd-3-clause
- Created: 2020-12-16T13:42:24.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2022-12-07T13:25:44.000Z (almost 3 years ago)
- Last Synced: 2024-04-20T20:55:21.587Z (over 1 year ago)
- Topics: mader, minvo, multi-agent, obstacle-avoidance, optimization, path-planning, planner, ros, trajectory-optimization
- Language: C++
- Homepage:
- Size: 123 MB
- Stars: 451
- Watchers: 15
- Forks: 74
- Open Issues: 4
-
Metadata Files:
- Readme: Readme.md
- License: LICENSE
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README
# MADER: Trajectory Planner in Multi-Agent and Dynamic Environments #
### **Accepted for publication in the IEEE Transactions on Robotics (T-RO)**
Single-Agent | Multi-Agent |
:-------------------------:|:-------------------------:|
[](https://www.youtube.com/watch?v=aoSoiZDfxGE "MADER: Trajectory Planner in Multi-Agent and Dynamic Environments") | [](https://www.youtube.com/watch?v=aoSoiZDfxGE "MADER: Trajectory Planner in Multi-Agent and Dynamic Environments") |
[](https://www.youtube.com/watch?v=aoSoiZDfxGE "MADER: Trajectory Planner in Multi-Agent and Dynamic Environments") | [](https://www.youtube.com/watch?v=aoSoiZDfxGE "MADER: Trajectory Planner in Multi-Agent and Dynamic Environments") |
## Citation
When using MADER, please cite [MADER: Trajectory Planner in Multi-Agent and Dynamic Environments](https://arxiv.org/abs/2010.11061) ([pdf](https://arxiv.org/abs/2010.11061), [video](https://www.youtube.com/watch?v=aoSoiZDfxGE)):
```bibtex
@article{tordesillas2020mader,
title={{MADER}: Trajectory Planner in Multi-Agent and Dynamic Environments},
author={Tordesillas, Jesus and How, Jonathan P},
journal={IEEE Transactions on Robotics},
year={2021},
publisher={IEEE}
}
```
## General Setup
### Not Using Docker
The backend optimizer is Gurobi. Please install the [Gurobi Optimizer](https://www.gurobi.com/products/gurobi-optimizer/), and test your installation typing `gurobi.sh` in the terminal. Have a look at [this section](#issues-when-installing-gurobi) if you have any issues.
Then simply run this commands:
```bash
cd ~/ && mkdir ws && cd ws && mkdir src && cd src
git clone https://github.com/mit-acl/mader.git
cd ..
bash src/mader/install_and_compile.sh
```
The script [install_and_compile.sh](https://github.com/mit-acl/mader/blob/master/install_and_compile.sh) will install [CGAL v4.12.4](https://www.cgal.org/), [GLPK](https://www.gnu.org/software/glpk/) and other ROS packages (check the script for details). It will also compile the repo. This bash script assumes that you already have ROS installed in your machine.
### Using Docker
Install Docker using [this steps](https://docs.docker.com/engine/install/ubuntu/#install-using-the-repository), and remove the need of `sudo` following [these steps](https://docs.docker.com/engine/install/linux-postinstall/). Then follow these steps:
```bash
cd ~/ && mkdir ws && cd ws && mkdir src && cd src
git clone https://github.com/mit-acl/mader.git
```
For Gurobi, you need to download gurobi.lic file from [Gurobi Web License Manager](https://license.gurobi.com/manager/licenses) (more info [here](https://www.gurobi.com/web-license-service/)). A gurobi.lic not obtained through WLS will **not** work on docker. Place your gurobi.lic in [docker](https://github.com/mit-acl/mader/docker) folder and execute these commands:
```bash
cd ./mader/mader/docker
docker build -t mader . #This will probably take several minutes
```
Once built, ```docker run --volume=$PWD/gurobi.lic:/opt/gurobi/gurobi.lic:ro -it mader```
Useful Docker commands
```bash
docker container ls -a #Show a list of the containers
docker rm $(docker ps -aq) #remove all the containers
docker image ls #Show a lis of the images
docker image rm XXX #remove a specific image
```
### Running Simulations
#### Single-agent
```bash
roslaunch mader single_agent_simulation.launch #If you are using docker, you may want to add rviz:=false (to disable the visualization)
```
Now you can press `G` (or click the option `2D Nav Goal` on the top bar of RVIZ) and click any goal for the drone.
With Docker
In Docker, you can do this by running `docker exec -it [ID of the container] bash` in a new terminal (you can find the ID with `docker container ls -a`), and then running `rostopic pub /SQ01s/term_goal geometry_msgs/PoseStamped '{header: {stamp: now, frame_id: "world"}, pose: {position: {x: 10, y: 0, z: 1}, orientation: {w: 1.0}}}'`
To run many single-agent simulations in different random environments, you can go to the `scripts` folder and execute `python run_many_sims_single_agent.py`.
#### Multi-agent
> **Note**: For a high number of agents, the performance of MADER improves with the number of CPUs available in your computer.
Open four terminals and run these commands:
```
roslaunch mader mader_general.launch type_of_environment:="dynamic_forest"
roslaunch mader many_drones.launch action:=start
roslaunch mader many_drones.launch action:=mader
roslaunch mader many_drones.launch action:=send_goal
```
(if you want to modify the drone radius, you can do so in `mader.yaml`). For the tables shown in the paper, the parameters (drone radius, max vel,...) used are also detailed in the corresponding section of the paper
#### Octopus Search
You can run the octopus search with a dynamic obstacle by simply running
```
roslaunch mader octopus_search.launch
```
And you should obtain this:
(note that the octopus search has some randomness in it, so you may obtain a different result each time you run it).
## Issues when installing Gurobi:
If you find the error:
```
“gurobi_continuous.cpp:(.text.startup+0x74): undefined reference to
`GRBModel::set(GRB_StringAttr, std::__cxx11::basic_string, std::allocator > const&)'”
```
The solution is:
```bash
cd /opt/gurobi800/linux64/src/build #Note that the name of the folder gurobi800 changes according to the Gurobi version
sudo make
sudo cp libgurobi_c++.a ../../lib/
```
## Credits:
This package uses some C++ classes from the [DecompROS](https://github.com/sikang/DecompROS) repo (included in the `thirdparty` folder).
Note
We strongly recommend the use of `Gurobi` as the backend optimizer. Alternatively, you can use [`NLOPT`](https://nlopt.readthedocs.io/en/latest/) by setting `USE_GUROBI` to `OFF` in the [CMakeList.txt](https://github.com/mit-acl/mader/blob/master/mader/CMakeLists.txt), and then running `bash src/mader/install_nlopt.sh` before running `bash src/mader/install_and_compile.sh`.
---------
> **Approval for release**: This code was approved for release by The Boeing Company in December 2020.