hopper-mpc-point

Table of Contents

• Intro
• Setup
• Examples

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Intro

This repository contains Python code for a simple simulation of a hopping point mass with model predictive control. The simulator uses RK4 integration (alternatively, you can directly use the DT system).

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Setup

1. Clone this directory wherever you want.

git clone https://github.com/bbokser/hopper-mpc-point.git

2. Make sure both Python 3.8 and pip are installed.

sudo apt install python3.8
sudo apt-get install python3-pip
python3.8 -m pip install --upgrade pip

2. I recommend setting up a virtual environment for this, as it requires the use of a number of specific Python packages.

sudo apt-get install python3.8-venv
cd hopper-mpc-simple
python3.8 -m venv env

For more information on virtual environments: https://docs.python.org/3/library/venv.html

3. Activate the virtual environment, and then install numpy, scipy, matplotlib, sympy, cvxpy, and argparse.

source env/bin/activate
python3.8 -m pip install numpy scipy matplotlib cvxpy argparse

Don't use sudo here if you can help it, because it may modify your path and install the packages outside of the venv.

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Examples

Here is some example code:

cd hopper-mpc-point/src
source env/bin/activate
python3.8 run.py 2 mpc

This simulates the "robot" in 2D with mpc. The output is a set of plots tracking the behavior over time.

To simulate with mpc in 3D:

python3.8 run.py 3 mpc

To simulate with trajectory optimized open loop control:

python3.8 run.py 2 openloop

python3.8 run.py 3 openloop