I keep here my Jupyter notebooks on R&D on Model Predictive Control applyied to path-following problems in driverless vehicles. Includes also a Pybullet simulation to showcase the results. This mainly uses CVXPY as a framework. This repo contains code from other projecs, check them out in the special thanks section.

From within the mpc_pybullet_demo directory:

• To run the pybullet demo:

python3 mpc_demo_pybullet.py

• To run the simulation-less demo (simpler demo that does not use pybullet, useful for debugging):

python3 mpc_demo_pybullet.py

In both cases the script will promt the user for enter before starting the demo.

The settings for tuning the MPC controller are in the mpc_config class.

1. State space model derivation -> analytical and numerical derivaion of the model

2. MPC -> implementation and testing of various tweaks/improvements

3. Obstacle Avoidance -> Using halfplane constrains to avaoid track collisions -> Sill work in progress!

Racing car model is from: https://github.com/erwincoumans/pybullet_robots.

Results:

The environment can be repoduced via conda:

conda env create -f env.yml
conda activate simulation

The dependencies for just the python scripts can also be installed using pip:

pip3 install --user --requirement requirements.txt

• Prof. Borrelli - mpc papers and material
• AtsushiSakai - pythonrobotics
• erwincoumans - pybullet
• alexliniger - mpcc and his paper
• arex18 - rocket-lander