Perceptive and torque-control NMPC wiki


The framework has been written and tested using Ubuntu 18.04, since it is the OS used by the LAAS-CNRS robotic platform. It should work seamlessly on a recent Linux version, but there is no guarantee.
Some issues has been found while installing the software on Ubuntu 16.04 because of version incompatibility with Protoc and Protobuf.
The installation on a non-Linux OS has to be handled by the user.

The installation assumes the use of a package manager (e.g. apt) to install some dependencies, as well as the Gazebo simulator. Everything provided in this repo or by the LAAS-CNRS robotic platform aims to be installed locally in the repository folder to avoid polluting the user's system.

I - Software Overview

I.1. Openrobots

Collections of all the open-source software used at LAAS. You can find more details in Openrobots Wiki-Homepage

I-2. Robotpkg

Robotpkg is a packaging system for installing robotics software developed by the robotic community.
We will use robotpkg to install the required modules for the simulations (state estimation, gazebo interface...) as well as third-party dependencies (qpOases).

I-3. GenoM

GenoM is a generator of modules, designed to be middleware independant, i.e. the same module can be compiled for, e.g., ROS, YARP, or Pocolibs, without any modification.
This allows a great code re-usability and to abstract the user from any specific choice of a middleware.
Originally GenoM has been developed tightly with Pocolibs, then from version 3, aka GenoM3, ROS templates has been provided.

Another specificity of GenoM is the interaction with and between components.
Each component is started independantly like a linux executable (within a roscore, for ROS, or a h2 intance, for Pocolibs), then the connection between ports (or topics) is made using a supervisor, Genomix, either with Python, Matlab or TCL.

I-4. Pocolibs

Pocolibs is a middleware, like ROS.
It aims at being lighter and faster than ROS, when running on a single machine, thanks to the exploitation of shared memory. ROS, on the other hand, uses a network layer for sending messages between nodes, this leads to greater delays and loss of performances.

I-5. TeleKyb

The TeleKyb software platform provides the aerial-robotic oriented softwares developped at LAAS-CNRS.
In particular, we will use:
  • mrsim, a Multi-Robot SIMulator. It is design to be a transparent interface w.r.t. the real aerial vehicles used in LAAS-CNRS. It makes the transition between simulation and experiment transparent, from the software point of view.
  • pom, a UKF-based state estimator merging state feedback for different sources (e.g. mocap + IMU)
  • optitrack,, to export the motion capture data to the genom software stack
  • rotorcraft, the low-level interface, with either the simulated or real platform
  • nhfc, near-hovering flight controller, used for unmodeled take-off and post-failure recoverues
  • maneuver, a global trajectory planner, providing position and attitude (as quaternions) as well as first and second derivatives. It implement take-off and waypoint-to-waypoint motions.

I-6. Gazebo

To simulate the platform, we use the Gazebo simulator. To interface it with the genom software stack, we use two dedicated components:
  • mrsim-gazebo a plugin to interface the simulated multi-rotor with the genom components (in place of mrsim)
  • optitrack-gazebo emulates the optitrack network interface to publish the model poses

The installation procedure for Gazebo can be found at

II - Installation procedure

This section is a tutorial on how to install the software architecture to run the simulations.

II-0. Clone the Perceptive and torque-control NMPC repository

Clone the repo associated to this project. Its root will act as the devel folder for the following.

git clone git://
cd ./perceptive-torque-nmpc/

To simplify the installation, we provide some environment variables in the file.
In order to run all the installed executables, we need to setup the path to the newly created folders.
We provide a script that exports all the required variables.

NOTE: the source has to be called in the repository root since it uses the pwd command to export the paths.


II-1. Setup robotpkg

(Steps taken from

1. Clone the robotpkg lastest release:

git clone git://

2. Check that the openrobots/ folder exists in the repository root, and update the environement variables accordingly if you didn't source the file:

export ROBOTPKG_BASE=`pwd`/openrobots

3. Install robotpkg

cd robotpkg/bootstrap
./bootstrap --prefix=$ROBOTPKG_BASE

4. Install the required components and their dependencies

The installation can be done 'manually' by navigating to the desired folder in ./robotpkg/ and install with make update; but we will simplify the process using a set.
To do so, we need to edit the config file: $ROBOTPKG_BASE/etc/robotpkg.conf. Add the following at the end of the file:

PKG_OPTIONS.%-genom3 = \
        codels \
        pocolibs-server \

PKGSET.mpcset = \
    sysutils/arduio-genom3 \
    architecture/genom3 \
    architecture/genom3-pocolibs \
    robots/rotorcraft-genom3 \
    localization/pom-genom3 \
    localization/optitrack-genom3 \
    motion/nhfc-genom3 \
    path/libkdtp \
    optimization/qpoases \
    net/genomix \
    supervision/matlab-genomix \
    supervision/tcl-genomix \
    shell/eltclsh \
    simulation/mrsim-genom3 \
    simulation/mrsim-gazebo \
    simulation/libmrsim \

PREFER.lapack = robotpkg
PREFIX.matlab = <path/to/Matlab>

The last line need to point to the Matlab root folder in the system (e.g. /opt/Matlab).
It is recommanded to use Matlab for the proposed simulations since the syntax is more intuitive and comprehensible for the user to modify them. However, we also provide all the launch files in tcl, as well as the environment to run them (shell/eltclsh in the above list is a custom tcl script shell).
If Matlab is not installed on the system, remove the lines supervision/matlab-genomix \ and PREFIX.matlab = <path/to/Matlab> from the above list.
Also, all the above is meant for using Pocolibs, not ROS. Futur version of this tutorial might come to use the ROS install.

Now return to the robotpkg folder and install all the set:

cd robotpkg
make update-mpcset

During the installation, some required dependencies need to be install with the usual package manager (e.g. apt on Ubuntu). When the install stops, install the required packages and rerun the above command.

5. Matlab configuration

The last step is to update Matlab path to use the custom libraries, if relevant.
Add the following paths in the Matlab path window:


(change </path/to/openrobots> to the value of $ROBOTPKG_BASE)

II-2. Install custom components

List of the components

The src/ folder contains some additional components, in particular:
  • vision-idl: the type declaration regarding the camera modules
  • camgazebo-genom3: read the data from the gazebo inate cameras, via the gazebo API
  • camviz-genom3: record and/or display the images from a camera
  • arucotag-genom3: detect the ArUco markers
  • maneuver-genom3: custom version of maneuver (already mentionned) that publishes the reference trajectory for a specified receding horizon
  • uavmpc-genom3: the NMPC controller presented in the manuscript
  • tagodom-genom3: an ESEKF-based SLAM implementation, following the formalism introduced in the manuscript

Install the extra components

Since it they are not considered 'stable' as the one provided in robotpkg, we rather install them in a devel folder.
Go to the project root, check that the devel folder exists, export the path if you didn't source the Then go to the sources folder:

export DEVEL_BASE=`pwd`/devel
cd src/

For the manual installation, asciidoctor is needed. It can be installed using apt or any package manager.
Each component here has to be installed manually, using autoconf. To do so, proceed as follow:

cd src/<component>/
mkdir build
cd build
../configure --prefix=$DEVEL_BASE --with-templates=pocolibs/client/c,pocolibs/server
make install

The component vision-idl has to be installed first since it defines some type headers used by others.
The installation of the main component, uavmpc-genom3, is described in the next subection.

Install the MPC controller

Before installing the MPC controller, we have to generate the C sources corresponding to the desired model.
To do so, go to the model_generation/ folder:

cd src/uavmpc-genom3/model_generation

There is a file there, explaining the requirements.
In short, the model sources are exported to C using CasADi in python3.
python3 along with NumPy, SciPy and CasADi are required, and easily installable on most Linux distributions (e.g. with apt and pip3).
Then, the sources are generated using:

python3 <quad or hexa>

Then install the component as explained before, but add the following flags to the configure command:
configure --prefix=$GENOM_DEVEL --with-templates=pocolibs/client/c,pocolibs/server CPPFLAGS="-Wall -march=native -mfpmath=sse -I$ROBOTPKG_BASE/include" CXXFLAGS="-O3" CFLAGS="-O3" LDFLAGS="-L$ROBOTPKG_BASE/lib -Wl,-R$ROBOTPKG_BASE/lib" 

II-3. Setup the environment

In order to run all the installed executables, we need to setup the path to the newly created folders.
All the required variables are exported in the file.

III - Running the simulation

We ws/ folder contains all the material to run a basic simulation with the NMPC.
In a terminal, launch the script. It starts all the genom components, in background. It is used as a console since it displays warnings or error during runtime.
In another terminal, start gazebo with one of the world file provided.
Finally, run matlab..

The init.m file is the main entry point. It contains all the manual parameters one would change, then perform the call to the init_genom.m function accordingly.
Then the traj_mpc_tagodom.m script can be launched.
In particular, the type of UAV can be changed by setting param.plaform to QRO or TX0 (resp. QuadRotor and TiltHex).

It performs the initialization of the components, and sends the instructions as a sequences, which the user can enable by pressing Enter, step by step.

NOTE: Wait that the propellers are spinning at full velocity before initiating the take-off.
This is a mandatory trick to allow the MPC to takeoff without any modelling of the ground reaction force.

The section %% MOTION could be modified to give any motion to the UAV.

NOTE: To interact with the trajectory generator, maneuver, use the following commands:, Y, Z, YAW, TIME);

using the desired values of parameters.

Updated by Martin Jacquet almost 2 years ago · 7 revisions