This repository is my attempt at implementing Model Predictive Path Integral Control. I have taken the setup from the original repository (https://github.com/MizuhoAOKI/python_simple_mppi) and implemented my own version in it. TODO: implement CBF instead of current collision check

Model Predictive Path-Integral (MPPI) Control [G. Williams et al., 2018] is a promising sampling-based optimal control algorithm.
This repository is for understanding the basic idea of the algorithm.

• python

  • version 3.10 or higher is recommended.

• poetry

  • seting up python environment easily and safely.
  • only numpy, matplotlib, notebook are needed to run all scripts in this repository.

• ffmpeg

  • mp4 movie writer

  • installation details

    • For Ubuntu Users
      • sudo apt-get update
      • sudo apt-get -y install ffmpeg
    • For Windows Users
      • Install scoop
      • scoop install ffmpeg
    • For macOS Users
      • Install homebrew
      • brew install ffmpeg
    • Check the official website if necessary
      • https://ffmpeg.org/

git clone https://github.com/MizuhoAOKI/python_simple_mppi.git
cd python_simple_mppi
poetry install

• Run simulation

  cd python_simple_mppi
  poetry run python scripts/mppi_pathtracking.py

• Run jupyter notebook if you would like to check mathematical explanations on the algorithm.

  cd python_simple_mppi
  poetry run jupyter notebook notebooks/mppi_pathtracking.ipynb

• Run simulation

  cd python_simple_mppi
  poetry run python scripts/mppi_pathtracking_obav.py

• Run jupyter notebook if you would like to check mathematical explanations on the algorithm.

  cd python_simple_mppi
  poetry run jupyter notebook notebooks/mppi_pathtracking_obav.ipynb

• Run simulation to swing up a pendulum.

  cd python_simple_mppi
  poetry run python scripts/mppi_pendulum.py

• Run jupyter notebook if you would like to check mathematical explanations on the algorithm.

  cd python_simple_mppi
  poetry run jupyter notebook notebooks/mppi_pendulum.ipynb
  
  

1. G. Williams et al. "Information-Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving"
   • URL : https://ieeexplore.ieee.org/document/8558663
   • PDF : https://arxiv.org/pdf/1707.02342.pdf