Control strategies for rotary wing Micro Aerial Vehicles (MAVs) using ROS

This repository contains controllers for rotary wing MAVs. Currently we support the following controllers:

• mav_linear_mpc : Linear MPC for MAV trajectory tracking
• mav_nonlinear_mpc : Nonlinear MPC for MAV trajectory tracking
• PID_attitude_control : low level PID attitude controller

Moreover, an external disturbance observer based on Kalman Filter is implemented to achieve offset-free tracking.

If you use any of these controllers within your research, please cite one of the following references

@incollection{kamelmpc2016,
                author      = "Mina Kamel and Thomas Stastny and Kostas Alexis and Roland Siegwart",
                title       = "Model Predictive Control for Trajectory Tracking of Unmanned Aerial Vehicles Using Robot Operating System",
                editor      = "Anis Koubaa",
                booktitle   = "Robot Operating System (ROS) The Complete Reference, Volume 2",
                publisher   = "Springer",
                year = “2017”,
                note  = "(to appear)",
}

@ARTICLE{2016arXiv161109240K,
          author = {{Kamel}, M. and {Burri}, M. and {Siegwart}, R.},
          title = "{Linear vs Nonlinear MPC for Trajectory Tracking Applied to Rotary Wing Micro Aerial Vehicles}",
          journal = {ArXiv e-prints},
          archivePrefix = "arXiv",
          eprint = {1611.09240},
          primaryClass = "cs.RO",
          keywords = {Computer Science - Robotics},
          year = 2016,
          month = nov
}

To run the controller with RotorS simulator (https://github.com/ethz-asl/rotors_simulator), follow these instructions:

• Install and initialize ROS indigo desktop full, additional ROS packages, catkin-tools:

  $ sudo sh -c 'echo "deb http://packages.ros.org/ros/ubuntu `lsb_release -sc` main" > /etc/apt/sources.list.d/ros-latest.list'
  $ wget http://packages.ros.org/ros.key -O - | sudo apt-key add -
  $ sudo apt-get update
  $ sudo apt-get install ros-indigo-desktop-full ros-indigo-joy ros-indigo-octomap-ros python-wstool python-catkin-tools
  $ sudo rosdep init
  $ rosdep update
  $ source /opt/ros/indigo/setup.bash

• Initialize catkin workspace:

  $ mkdir -p ~/catkin_ws/src
  $ cd ~/catkin_ws
  $ catkin config --cmake-args -DCMAKE_BUILD_TYPE=Release
  $ catkin init  # initialize your catkin workspace

• Get the controllers and dependencies

  $ sudo apt-get install liblapacke-dev
  $ git clone https://github.com/catkin/catkin_simple.git
  $ git clone https://github.com/ethz-asl/rotors_simulator.git
  $ git clone https://github.com/ethz-asl/mav_comm.git
  $ git clone https://github.com/ethz-asl/eigen_catkin.git

  $ git clone https://github.com/ethz-asl/mav_control_rw.git

• Build the workspace

  $ catkin build

• Run the simulator and the linear MPC. In seperate terminals run the following commands

  $ roslaunch rotors_gazebo mav.launch mav_name:=firefly

  $ roslaunch mav_linear_mpc mav_linear_mpc_sim.launch mav_name:=firefly

You can use rqt to publish commands to the controller.

To run the controller with the multi sensor fusion (MSF) framewok (https://github.com/ethz-asl/ethzasl_msf):

• Get msf

  $ git clone https://github.com/ethz-asl/ethzasl_msf.git

• Run the simulator, the linear MPC and MSF, in seperate terminals run the following commands

  $ roslaunch rotors_gazebo mav.launch mav_name:=firefly

  $ roslaunch mav_linear_mpc mav_linear_mpc_sim_msf.launch mav_name:=firefly

Don't forget to initialize MSF.

The linear and nonlinear MPC controllers publish and subscribe to the following topics:

• Published topics:

  • command/roll_pitch_yawrate_thrust of type mav_msgs/RollPitchYawrateThrust. This is the command to the low level controller. Angles are in rad and thrust is in N.
  • command/current_reference of type trajectory_msgs/MultiDOFJointTrajectory. This is the current reference.
  • state_machine/state_info of type std_msgs/String. This is the current state of the state machine of mav_control_interface.
  • predicted_state of type visualization_msgs/Marker. This is the predicted vehicle positions that can be used for visualization in rviz.
  • reference_trajectory of type visualization_msgs/Marker. This is the reference trajectory that can be used for visualization in rviz.
  • KF_observer/observer_state of type mav_disturbance_observer/ObserverState. This is the disturbance observer state used for debugging purposes. It includes estimated external forces and torques.

• Subscribed topics:

  • command/pose of type geometry_msgs/PoseStamped. This is a reference set point.
  • command/trajectory of type trajectory_msgs/MultiDOFJointTrajectory. This is a desired trajectory reference that includes desired velocities and accelerations.
  • rc of type sensor_msgs/Joy. This is the remote control commands for teleoperation purposes. It also serves to abort mission anytime.
  • odometry of type nav_msgs/Odometry. This is the current state of the vehicle. The odometry msg includes pose and twist information.

The PID attitude controller publishes and subscribes to the following topics:

• Published topics:

  • command/motor_speed of type mav_msgs/Actuators. This is the commanded motor speed.

• Subscribed topics:

  • command/roll_pitch_yawrate_thrust of type mav_msgs/RollPitchYawrateThrust.
  • odometry of type nav_msgs/Odometry.

A summary of the linear and nonlinear MPC parameters:

* Through dynamic reconfigure, it is possible to change these parameters.

A summary of the PID attitude parameters:

* Through dynamic reconfigure, it is possible to change these parameters.

[1] Model Predictive Control for Trajectory Tracking of Unmanned Aerial Vehicles Using Robot Operating System. Mina Kamel, Thomas Stastny, Kostas Alexis and Roland Siegwart. Robot Operating System (ROS) The Complete Reference Volume 2. Springer 2017 (to appear)

[2] Linear vs Nonlinear MPC for Trajectory Tracking Applied to Rotary Wing Micro Aerial Vehicles. Mina Kamel, Michael Burri and Roland Siegwart. arXiv:1611.09240

Mina Kamel fmina(at)ethz.ch