/mav_control_rw

Control strategies for rotary wing Micro Aerial Vehicles using ROS

Primary LanguageCApache License 2.0Apache-2.0

RotorS MPC Controller

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All credits for the control structure and development are given to ETH ASL. The original repository can be found: https://github.com/ethz-asl/mav_control_rw

The repository was required to be re-uploaded since the Georgia Tech Github is not connected to Github

DroneX Specific README Details

In order to make changes to the optimal control calculations in the nonlinear mpc, follow these steps that are found in the directory: mav_nonlinear_mpc/solver_made_from_cpp

First, make sure ACADO toolkit is installed by going to there website. Then add this line to your ~/.bashrc and source it:

source [pathtoAcado]/build/acado_env.sh

From the folder: solver_made_from_cpp

Edit the nmpc_solver_setup.cpp file with whatever changes are desired. Then,

mkdir build
cd build
cmake ..
make

The executable files are created in the /solver folder.

Execute the solver by:

cd ../../solver
./nmpc_solver_setup

Then, build the entire workspace by:

cd ~/my_catkin_workspace
catkin build

DEFAULT README DETAILS

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:

  • 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”,
}
@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:

  • 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.

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:

  • 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.

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