#DENMPC

@file    README.md
@Author  Jan Dentler (jan.dentler@uni.lu)
         University of Luxembourg
@date    26.October, 2017
@time    16:14h
@license GPLv3
@brief   README

Outline

DENMPC is providing an object-oriented real-time nonlinear model predictive control (NMPC) framework which has been developed at the Automation & Robotics Research Group http://wwwde.uni.lu/snt/research/automation_robotics_research_group at the University of Luxembourg.

The basic idea of DENMPC is to provide a fast nonlinear MPC that can adjust at runtime to different systems. This refers to:

• Multi-agent systems that can change tasks, objectives and topology
• Fault-tolerant control, where the controller has to adapt to different system conditions
• Control prototyping, where you want to explore different scenarios without creating the underlying Optimal Control Problem (OCP) from scratch

In order to do so, DENMPC features an object-oriented modularization approach. This allows structuring the control scenario into agents, constraints and couplings. Out of these single components, DENMPC is dynamically creating the OCP at runtime. As a result, agents, constraints and couplings can be added, removed, and parameters can be changed at runtime. This addition, respectively subtraction is triggered by events which can be for example timer events, ROS-messages events, etc. For very complex tasks, this can further be used to combine step chains with DENMPC, to specialize the MPC for each task stage individually.

##Literature and Publication DENMPC is open-source software, available under available under https://github.com/snt-robotics/denmpc and https://github.com/DentOpt/denmpc. The usage of DENMPC use regulated under the terms of the GPL3 license (Proprietary licences are available under request.). If you are using the software in your research work, you are supposed to cite one or more of the following references:

J. Dentler, 
"Real-time Model Predictive Control of Cooperative Aerial Manipulation",
[http://orbilu.uni.lu/handle/10993/36965](http://orbilu.uni.lu/handle/10993/36965),
PhD Thesis, University of Luxembourg, July 2018

Jan Dentler, Somasundar Kannan, Souad Bezzaoucha, Miguel Angel Olivares-Mendez, and Holger Voos, 
Model predictive cooperative localization control of multiple UAVs using potential function sensor constraints. 
Autonomous Robots, March 2018, pages 1–26.
doi: 10.1007/s10514-018-9711-z, url: https://doi.org/10.1007/s10514-018-9711-z

J. Dentler, S. Kannan, M. A. O. Mendez and H. Voos,
"A modularization approach for nonlinear model predictive control of distributed fast systems",
24th Mediterranean Conference on Control and Automation (MED), Athens, Greece, 2016, pp. 292-297.
doi: 10.1109/MED.2016.7535973

Jan Dentler and Somasundar Kannan and Miguel Angel Olivares Mendez and Holger Voos,
"A real-time model predictive position control with collision avoidance for commercial low-cost quadrotors",
Proceedings of 2016 IEEE Multi-Conference on Systems and Control (MSC 2016), Argentina, Buenos Aires, 2016 

If you are using the "Condensed Multiple Shooting Generalized Minimal Residuum Method (CMSCGMRES)" kernel contributed by the team of Prof. Dr. Toshiyuki OHTSUKA, please refer to:

Ohtsuka, T.,
“A Continuation/GMRES Method for Fast Computation of Nonlinear Receding Horizon Control,”
Automatica, Vol. 40, No. 4, Apr. 2004, pp. 563-574.

Seguchi, H., and Ohtsuka, T.,
“Nonlinear Receding Horizon Control of an Underactuated Hovercraft,”
International Journal of Robust and Nonlinear Control, Vol. 13, Nos. 3-4, Mar.-Apr. 2003, pp. 381-398. 

DENMPC features:

Nonlinear model predictive control (e.g. a quadrotor with nonlinear system dynamics)
Central control of single-agent systems (e.g. a single robot)
Central control of multi-agent systems (e.g. multiple robots that are interacting)
Object-oriented code to easily adapt it:
    Controller: Interface class for implementations of controllers, e.g.CMSCGMRES
    Agent: Interface class for implementations of agents, respective system or robot types, e.g. Quadrotor
    Constraint: Interface class for implementations of single-agent constraints
    Coupling: Interface class for implementations for coupling agents 
Open-source code 

Installation

# Navigate to your ROS catkin workspace (e.g. catkin_ws):`
cd catkin_ws/src
#Clone repository
git clone https://github.com/DentOpt/denmpc.git
cd ..
#Build package
catkin_make

To use the AR.Drone 2.0 scenario with the tum simulator

Install:

cd catkin_ws/src
git clone https://github.com/DentOpt/ardrone_simulator_gazebo7.git
cd ..
catkin_make

To run the AR.Drone 2.0 scenario in gazebo, run

roslaunch cvg_sim\_gazebo ardrone_testworld.launch 

Launch drone (Takeoff) from commandline:

rostopic pub -1 /ardrone/takeoff std_msgs/Empty

The AR.Drone 2.0 simulator is configured to subscribe control commands under the topic "/cmd_vel". The AR.Drone 2.0 pose is published under "/pose"

To control the AR.Drone 2.0 in with denmpc: either to track center of UAV:

rosrun denmpc scenario_ardrone_pose_tracking_node

or to track with sensor constraint:

rosrun denmpc scenario_ardrone_sensor_tracking_node

and to send desired pose use rqt or commandline, e.g

rostopic pub /desiredpose geometry_msgs/PoseStamd '{header: {stamp: now, frame_id: "map"}, pose: {position: {x: 0.0, y: 0.0, z: 2.0}, orientation: {x: 0.0, y: 0.0, z: 0.0, w: 1.0}}}'

To use the Turtlebot scenario

Install:

cd catkin_ws/src
sudo apt-get install
git clone https://github.com/ros/ros_tutorials.git #Install Turtlesim
git clone https://github.com/DentOpt/denmpc.git -b tutorial_turtlesim  #Install DENMPC branch
cd ..
catkin_make

Run:

roscore #run roscore
rosrun turtlesim turtlesim_node #Run Turtlesim in separate tab
rosrun denmpc scenario_scenario_node #Run denmpc in separate tab

That's it! You will see how the turtle DENMPC moves from its initial position to the position x=1 y=1. You can give any desired position by publishing it to the /turtle1/desiredpose. For example, for the new target x=5, y=5 type rostopic pub /turtle1/desiredpose turtlesim/Pose "{x: 5.0, y: 5.0, theta: 0.0, linear_velocity: 0.0, angular_velocity: 0.0}"