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ethz-asl / mav_control_rw

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

Overview

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

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”,
}
@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
}

Installation instructions

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

  $ 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
  $ 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):

  $ 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.

Supported autopilots

Asctec Research Platforms

This control will work as is with the ros interface to the now discontinued Asctec research platforms (Hummingbird, Pelican, Firefly and Neo).

Pixhawk

This controller requires some small modifications to the PX4 firmware to allow yaw rate inputs. A modified version of the firmware can be found here. The firmware is interfaced with through a modified mavros node.

DJI

The controller can interface with DJI platforms through our mav_dji_ros_interface

Published and subscribed topics

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

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

Parameters

A summary of the linear and nonlinear MPC parameters:

Parameter Description
use_rc_teleop enable RC teleoperation. Set to false in case of simulation.
reference_frame the name of the reference frame.
verbose controller prints on screen debugging information and computation time
mass vehicle mass
roll_time_constant time constant of roll first order model
pitch_time_constant time constant of pitch first order model
roll_gain gain of roll first order model
pitch_gain gain of pitch first order model
drag_coefficients drag on x,y,z axes
q_x, q_y, q_z* penalty on position error
q_vx, q_vy, q_vz* penalty on velocity error
q_roll, q_pitch* penalty on attitude state
r_roll, r_pitch, r_thtust* penalty on control input
r_droll, r_dpitch, r_dthtust* penalty on delta control input (only Linear MPC)
roll_max, pitch_max, yaw_rate_max* limits of control input
thrust_min, thrust_max* limit on thrust control input in m/s^2
K_yaw* yaw P loop gain
Ki_xy, Ki_z* integrator gains on xy and z axes respectively
position_error_integration_limit limit of position error integration
antiwindup_ball if the error is larger than this ball, no integral action is applied
enable_offset_free* use estimated disturbances to achieve offset free tracking
enable_integrator* use error integration to achieve offset free tracking
sampling_time the controller sampling time (must be equal to the rate of odometry message
prediction_sampling_time the prediction sampling time inside the controller

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

A summary of the PID attitude parameters:

Parameter Description
inertia vehicle inertia 3x3 matrix
allocation_matrix control allocation matrix depending on the configuration of the rotors
n_rotors number of rotors
rotor_force_constant force constant of the rotor in N/rad^2 such that F_i =rotor_force_constant*rotor_velocity^2
rotor_moment_constant rotor moment constant such that M = rotor_moment_constant*F_i
arm_length distance between rotor and vehicle center
roll_gain, pitch_gain* error proportional term
p_gain, q_gain, r_gain* derivative gain
roll_int_gain, pitch_int_gain* integrator gains
max_integrator_error saturation on the integrator

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

References

[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

Contact

Mina Kamel fmina(at)ethz.ch