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MADER: Trajectory Planner in Multi-Agent and Dynamic Environments #\n\n\n### **Accepted for publication in the IEEE Transactions on Robotics (T-RO)**\n\n\nSingle-Agent               |  Multi-Agent           | \n:-------------------------:|:-------------------------:|\n[![MADER: Trajectory Planner in Multi-Agent and Dynamic Environments](./mader/imgs/single_agent1.gif)](https://www.youtube.com/watch?v=aoSoiZDfxGE \"MADER: Trajectory Planner in Multi-Agent and Dynamic Environments\")      |  [![MADER: Trajectory Planner in Multi-Agent and Dynamic Environments](./mader/imgs/circle.gif)](https://www.youtube.com/watch?v=aoSoiZDfxGE \"MADER: Trajectory Planner in Multi-Agent and Dynamic Environments\") |  \n[![MADER: Trajectory Planner in Multi-Agent and Dynamic Environments](./mader/imgs/single_agent2.gif)](https://www.youtube.com/watch?v=aoSoiZDfxGE \"MADER: Trajectory Planner in Multi-Agent and Dynamic Environments\")       |  [![MADER: Trajectory Planner in Multi-Agent and Dynamic Environments](./mader/imgs/sphere.gif)](https://www.youtube.com/watch?v=aoSoiZDfxGE \"MADER: Trajectory Planner in Multi-Agent and Dynamic Environments\")    |  \n\n## Citation\n\nWhen 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)):\n\n```bibtex\n@article{tordesillas2020mader,\n  title={{MADER}: Trajectory Planner in Multi-Agent and Dynamic Environments},\n  author={Tordesillas, Jesus and How, Jonathan P},\n  journal={IEEE Transactions on Robotics},\n  year={2021},\n  publisher={IEEE}\n}\n```\n\n## General Setup\n\n### Not Using Docker\n\nThe 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.\n\nThen simply run this commands:\n\n```bash\ncd ~/ \u0026\u0026 mkdir ws \u0026\u0026 cd ws \u0026\u0026 mkdir src \u0026\u0026 cd src\ngit clone https://github.com/mit-acl/mader.git\ncd ..\nbash src/mader/install_and_compile.sh      \n```\n\nThe 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. \n\n### Using Docker\n\nInstall 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:\n\n```bash\ncd ~/ \u0026\u0026 mkdir ws \u0026\u0026 cd ws \u0026\u0026 mkdir src \u0026\u0026 cd src\ngit clone https://github.com/mit-acl/mader.git\n```\n\nFor 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:\n\n```bash\ncd ./mader/mader/docker\ndocker build -t mader . #This will probably take several minutes\n```\nOnce built, ```docker run --volume=$PWD/gurobi.lic:/opt/gurobi/gurobi.lic:ro -it mader```\n\n\u003cdetails\u003e\n  \u003csummary\u003e \u003cb\u003eUseful Docker commands\u003c/b\u003e\u003c/summary\u003e\n  \n```bash\ndocker container ls -a  #Show a list of the containers\ndocker rm $(docker ps -aq) #remove all the containers\ndocker image ls #Show a lis of the images\ndocker image rm XXX #remove a specific image\n```\n\n\u003c/details\u003e\n\n### Running Simulations\n\n#### Single-agent\n```bash\nroslaunch mader single_agent_simulation.launch #If you are using docker, you may want to add rviz:=false (to disable the visualization)\n```\nNow you can press `G` (or click the option `2D Nav Goal` on the top bar of RVIZ) and click any goal for the drone. \n\n\u003cdetails\u003e\n  \u003csummary\u003e \u003cb\u003eWith Docker\u003c/b\u003e\u003c/summary\u003e\n  \nIn 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}}}'`\n\n\u003c/details\u003e\n\n\n\n\nTo 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`.\n\n#### Multi-agent\n\n\u003e **Note**: For a high number of agents, the performance of MADER improves with the number of CPUs available in your computer. \n\nOpen four terminals and run these commands:\n\n```\nroslaunch mader mader_general.launch type_of_environment:=\"dynamic_forest\"\nroslaunch mader many_drones.launch action:=start\nroslaunch mader many_drones.launch action:=mader\nroslaunch mader many_drones.launch action:=send_goal\n```\n\n(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\n\n\n#### Octopus Search\nYou can run the octopus search with a dynamic obstacle by simply running\n\n```\nroslaunch mader octopus_search.launch\n```\nAnd you should obtain this:\n\n![](./mader/imgs/octopus_search.png) \n\n(note that the octopus search has some randomness in it, so you may obtain a different result each time you run it).\n\n## Issues when installing Gurobi:\n\nIf you find the error:\n```\n“gurobi_continuous.cpp:(.text.startup+0x74): undefined reference to\n`GRBModel::set(GRB_StringAttr, std::__cxx11::basic_string\u003cchar,\nstd::char_traits\u003cchar\u003e, std::allocator\u003cchar\u003e \u003e const\u0026)'”\n```\nThe solution is:\n\n```bash\ncd /opt/gurobi800/linux64/src/build  #Note that the name of the folder gurobi800 changes according to the Gurobi version\nsudo make\nsudo cp libgurobi_c++.a ../../lib/\n```\n\n## Credits:\nThis package uses some C++ classes from the [DecompROS](https://github.com/sikang/DecompROS) repo (included in the `thirdparty` folder).\n\n\n\u003cdetails\u003e\n  \u003csummary\u003e \u003cb\u003eNote\u003c/b\u003e\u003c/summary\u003e\n\nWe 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`. \n\n\u003c/details\u003e\n\n---------\n\n\u003e **Approval for release**: This code was approved for release by The Boeing Company in December 2020. \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmit-acl%2Fmader","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmit-acl%2Fmader","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmit-acl%2Fmader/lists"}