Hierarchical Optimization Model Predictive Control

ROS 2 packages to implement generic controllers based on Hierarchical Optimization (HO) and Model Predictive Control (MPC).

Table of Contents

• Hierarchical Optimization Model Predictive Control
  • Table of Contents
  • Overview
  • Installation
    • Dependencies
  • Usage
    • Scripts
      • With ROS
      • With Python
  • Known Bugs
  • Author

Overview

Installation

These packages have been tested with ROS 2 Humble and ROS 2 Iron on an Ubuntu system.

To use Torch with an NVIDIA graphics card, it is necessary to install the NVIDIA drivers for Ubuntu. Here.

Dependencies

Ubuntu packages:

• git

Python 3 packages:

• casadi
• clarabel
• cvxpy
• matplotlib
• numpy
• osqp
• pandas
• proxsuite
• quadprog
• qpsolvers
• scipy
• reluqp
• torch
• torchaudio
• torchvision

Torch, torchaudio, and torchvision are necessary for the neural network approximator and ReLuQP.

ffmepg is required for saving the videos in .mp4 format.

Usage

When I write <some_text>, you have to change the value in the <>.

Create a GitHub SSH key. Just do it.

Create a workspace and clone the repo in the source directory (don't fucking call your workspace a generic <my_workspace>)

mkdir <my_workspace>
cd <my_workspace>
git clone --recursive [email protected]:ddebenedittis/hierarchical_optimization_mpc.git src/hierarchical_optimization_mpc

Build the workspace with

colcon build --symlink-install

Source the workspace with (you have to add it to the ~/.bashrc or do it on every newly opened terminal)

source install/setup.base

Scripts

With ROS

Single robot example

ros2 run hierarchical_optimization_mpc example_single_robot

Multi robot example

ros2 run hierarchical_optimization_mpc example_multi_robot

Toy problem 1 (multiple conflicting tasks to one robot)

ros2 run hierarchical_optimization_mpc toy_problem_1

With Python

Python is more verbose than ROS, but you can pass options

Multi robot exmaple

python3 src/hierarchical_optimization_mpc/hierarchical_optimization_mpc/example_multi_robot.py [--hierarchical {True, False}] [--n_robots [int,int]] [--solver {clarabel, osqp, proxqp, quadprog, reluqp}] [--visual_method {plot, save, none}]

Parameters:

• --hierarchical bool: if True, uses the hierarchical approach.
• --n_robots list[int]: Number of unicycles and omnidirectional robots (default [6,0]).
• --solver {clarabel, osqp, proxqp, quadprog, reluqp}: QP solver to use.
• --visual_method {plot, save, none}: how to display the results.

Known Bugs

No known bugs.

Author

Davide De Benedittis