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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

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