Multi agent system to coordinate multiple UAVs

This repository was tested with ubuntu 18.04, ROS melodic and gazebo 9.

This repository is a ROS package, therefore it is necessary to set up a ROS workspace with the necessary packages and build it.

First, source ROS and create a new workspace:

$ source /opt/ros/melodic/setup.bash
$ mkdir -p ~/jason_ros_ws/src
$ cd ~/jason_ros_ws/
$ catkin_make

Then, download the necessary packages:

$ cd ~/jason_ros_ws/src/
$ git clone https://github.com/Rezenders/mas_uav.git
$ git clone https://github.com/jason-lang/jason_ros.git

Download deps and build the workspace:

$ cd ~/jason_ros_ws/
$ apt update
$ rosdep install --from-paths src --ignore-src -r -y
$ catkin_make

Install some more deps:

$ rosrun mavros install_geographiclib_datasets.sh
$ apt install gradle

Also, this experiments use ardupilot SITL to simulate the UAVs so it is necessary to set it up. The instructions for installing it can be found in: https://ardupilot.org/dev/docs/building-setup-linux.html#building-setup-linux

After installing ardupilot add UFSC location into locations.txt

$ echo 'UFSC=-27.604033,-48.518363,21,0' >> /ardupilot/Tools/autotest/locations.txt

It is also possible to use gazebo with ardupilot SITL, in case you do not want to use it you can skip the following instructions.

First, it is necessary to install a plugin to enable ardupilot to communicate with gazebo.

$ sudo apt-get install libgazebo9-dev

$ cd ~/jason_ros_ws/src
$ git clone https://github.com/Rezenders/ardupilot_gazebo
$ cd ardupilot_gazebo
$ mkdir build
$ cd build
$ cmake ..
$ make -j4
$ sudo make install

echo 'source /usr/share/gazebo/setup.sh' >> ~/.bashrc

Set Path of Gazebo Models (Adapt the path to where to clone the repo)

echo 'export GAZEBO_MODEL_PATH=$GAZEBO_MODEL_PATH:~/jason_ros_ws/src/ardupilot_gazebo/models' >> ~/.bashrc

Set Path of Gazebo Worlds (Adapt the path to where to clone the repo)

echo 'export GAZEBO_RESOURCE_PATH=$GAZEBO_RESOURCE_PATH:~/jason_ros_ws/src/ardupilot_gazebo/worlds' >> ~/.bashrc

source ~/.bashrc

DONE !

For this experiment a simple mission was performed: the UAV had to (1) takeoff; (2) fly to a predefined waypoint; (3) return to home; and finally (4) land.

If you will not use gazebo:

$ sim_vehicle.py -v ArduCopter --map --console -L UFSC -I 0

If you will use gazebo (optional):

$ sim_vehicle.py -v ArduCopter -f gazebo-iris --map --console -L UFSC -I 0

If you want to use gazebo run it in another terminal (optional):

$ gazebo --verbose iris_arducopter_runway.world

Remeber to source the workspace:

$ source ~/jason_ros_ws/devel/setup.bash

Jason version:

$ roslaunch mas_uav single_uav_jason.launch

Python version:

$ roslaunch mas_uav single_uav_python.launch

You should see this as result:

In the context of S&R missions, it is really useful to have more than one UAV collaborating since when vehicles are equipped with buoys their flight autonomy time is reduced due to the increased payload. Hence, a good strategy to adopt is to have two types of UAVs working together: (i) the Scouts which are equipped with cameras and (ii) the Rescuers that are in possession of buoys, using the former to find victims and inform the latter about their location, which then deliver the buoys.

Thus, an application was designed to mimic a S&R mission that uses one Scout and two Rescuers agents working in cooperation. Firstly, the Scout takes off and flies over an area looking for victims. When a victim is located the agent informs the rescuers about the victim's position. When the rescuers receive information about a victim's location they negotiate to decide which one will deliver the buoy. The one that ends up in charge of the rescue takes off, flies to the designated position, drops a buoy, and then returns to the landing area to recharge and replace the buoy. For the sake of simplicity, scouts are only in charge to locate victims and the rescuers to drop buoys.

In this experiments it will be necessary to start 3 instances of ardupilot

If you will not use gazebo:

$ sim_vehicle.py -v ArduCopter --map --console -L UFSC -I 0
$ sim_vehicle.py -v ArduCopter --map --console -L UFSC -I 1
$ sim_vehicle.py -v ArduCopter --map --console -L UFSC -I 2

If you will use gazebo (optional):

$ sim_vehicle.py -v ArduCopter -f gazebo-iris --map --console -L UFSC -I 0
$ sim_vehicle.py -v ArduCopter -f gazebo-iris --map --console -L UFSC -I 1
$ sim_vehicle.py -v ArduCopter -f gazebo-iris --map --console -L UFSC -I 2

If you want to use gazebo run it in another terminal (optional):

$ gazebo --verbose iris_arducopter_3uav.world

Note: In this experiment there may be some bugs while using gazebo

Remeber to source the workspace:

$ source ~/jason_ros_ws/devel/setup.bash

Jason version:

$ roslaunch mas_uav multiple_uav_jason_internal.launch

Python version:

$ roslaunch mas_uav multiple_uav_python_internal.launch

You should see this as result:

• Dockerfile shall be created/updated for this project
• Dockerfile-compose shall be created/update for this project
• It should be possible to run the docker-compose with balena
• Gazebo world and models shall be improved
• ardupilot_gazebo should be turned into a ROS package

If you find this repository usefull and use it in your project, please consider citing it:

@article{silva2020embedded,
  title={Embedded Architecture Composed of Cognitive Agents and ROS for Programming Intelligent Robots},
  author={Silva, Gustavo R and Becker, Leandro B and H{\"u}bner, Jomi F},
  journal={IFAC-PapersOnLine},
  volume={53},
  number={2},
  pages={10000--10005},
  year={2020},
  publisher={Elsevier}
}

Paper link