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MADER: Trajectory Planner in Multi-Agent and Dynamic Environments

Accepted for publication in the IEEE Transactions on Robotics (T-RO)

Single-Agent Multi-Agent
MADER: Trajectory Planner in Multi-Agent and Dynamic Environments MADER: Trajectory Planner in Multi-Agent and Dynamic Environments
MADER: Trajectory Planner in Multi-Agent and Dynamic Environments MADER: Trajectory Planner in Multi-Agent and Dynamic Environments

Citation

When using MADER, please cite MADER: Trajectory Planner in Multi-Agent and Dynamic Environments (pdf, video):

@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

MADER has been tested with

  • Ubuntu 16.04/ROS Kinetic
  • Ubuntu 18.04/ROS Melodic

The backend optimizer can be either Gurobi (recommended, used by default) or NLOPT (discouraged):

Then simply run this commands:

cd ~/ && mkdir ws && cd ws && mkdir src && cd src
git clone https://github.com/mit-acl/mader.git
cd ..
#bash src/mader/install_nlopt.sh      #ONLY if you are going to use NLOPT, it'll install NLopt v2.6.2
bash src/mader/install_and_compile.sh

The script install_and_compile.sh will install CGAL v4.12.4, 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.

Running Simulations

Single-agent

roslaunch mader single_agent_simulation.launch

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.

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<char,
std::char_traits<char>, std::allocator<char> > const&)'”

The solution is:

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 repo (included in the thirdparty folder).

Approval for release: This code was approved for release by The Boeing Company in December 2020.