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This repo contains instructions and code to reproduce the experiments from the paper in [link]()

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Full-Body Torque-Level Nonlinear Model Predictive Control for Aerial Manipulation

This repository contains the needed code to reproduce the experiments presented in the paper Full-Body Torque-Level Nonlinear Model Predictive Control for Aerial Manipulation.

Software dependencies

EagleMPC

  • Install the EagleMPC library at this tag and its dependencies.

⚠️ Crocoddyl version ⚠️ To get the same results as in the paper, you should checkout the Crocoddyl repository to this tag.

EagleMPC-ROS

The experiments to test the MPC controllers have been run in a simulated environment involving Gazebo and ROS Noetic. Make sure both are installed intro your computer.

Then, you need to clone and download the ROS packages in EagleMPC-ROS. Follow the installation instructions from the repository.

Experiments

To run the different experiments you first need to clone this repository into your computer.

cd <choose-your-path>
git clone https://github.com/PepMS/fbtl_nmpc_experiments.git

Trajectory optimization

To run a trajectory optimization experiment you need to do 2 steps:

  1. Modify the trajectory .yaml file with your paths. For example, open the hexacopter370_flying_arm_3_eagle_catch.yaml and change the urdf and the follow fields. Substitute the text between <> with the actual paths to the respective libraries.
trajectory:
  robot:
    name: "hexacopter_370_flying_arm_3"
    urdf: "<path-to-example-robot-data>/example-robot-data/robots/hexacopter370_description/urdf/hexacopter370_flying_arm_3.urdf"
    follow: "<path-to-ros-ws>/src/eagle_mpc_ros/eagle_mpc_yaml/multicopter/hexacopter370.yaml"
  1. Execute the selected Python script (with the display option in case that you want to visualize this in the Gepetto-Viewer). For example, for the Eagle's Catch case:
cd <choose-your-path>/fbtl_nmpc_experiments/trajectory-optimization
python3 eagle_catch.py display

nonLinear Model Predictive Control

To run an nMPC experiment. For example, the 4-Displacement experiment.

  1. Modify the file mpc/displacement/hexacopter370_flying_arm_3_mpc.yaml, which is placed inside the same folder as the Python script. Then, substitute the text between <> with the actual paths to the respective libraries.
    trajectory:
      robot:
        name: "hexacopter_370_flying_arm_3"
        urdf: "<path-to-example-robot-data>/example-robot-data/robots/hexacopter370_description/urdf/hexacopter370_flying_arm_3.urdf"
        follow: "<path-to-ros-ws>/src/eagle_mpc_ros/eagle_mpc_yaml/multicopter/hexacopter370.yaml"
  2. Analogously, modify the yaml file associated to the trajectory, either 4-displacement (hexacopter370_flying_arm_3_displacement.yaml for the Carrot and Rail case and hexacopter370_flying_arm_3_displacement_w.yaml for the Weighted controller) or the eagle_catch (hexacopter370_flying_arm_3_eagle_catch_nc.yaml). These are placed inside the eagle_mpc_yaml ROS package.
  3. Open the script of the experiment you want to run. For example mpc/displacement/displacement.py
  4. Set the variables in the section # -----VARIABLES----- to match your settings.
    1. mpcController allows you to select among the nMPC controllers (Weighted, Rail and Carrot)
    2. controller_settings_path is the path of the .yaml file contained in the same folder of the Python script
    3. controller_settings_destination is the location of the eagle_mpc_yaml ROS package
    4. For the disturbance case, you can also set the properties of the simulated disturbance

About

This repo contains instructions and code to reproduce the experiments from the paper in [link]()

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