PyRocketCraft

Control and land a rocket via deep learning or non-linear model predictive control (NMPC) in a 3D physics environment (using the pybullet engine). The non-linear MPC controller is using the acados library. The neural network parts are using pytorch.

Mail: [email protected]

Run the program with

(source env.sh; ./src/rocketcraft.py)

Installation on Linux and macOS

Run ./setup

Program structure

.
├── env.sh                      Setting up the env if coming back
├── torch_nn_mpc-rocket-vX.pth  Trained network to imitate NMPC
├── setup                       Setting up the project for first use
├── src/expert_collect.py       Generate data for training
├── src/expert_train.py         Train the neural network
├── src/geodetic_toolbox.py     Helper functions
├── src/modelrocket.urdf        Pybullet visualization and physics definition of the rocket
├── src/mpc
│   └── rocket_model.py         NMPC model and system dynamics definition
├── src/nnpolicy.py             Neural Network Controller
├── src/mpcpolicy.py            Model Predictive Control Module
├── src/rocketcraft.py          main entry point of application
└── src/simrocketenv.py         Physics simulation with gym interface, using pybullet

Block diagram:

The main function in rocketcraft.py runs the NMPC code decoupled from the physics simulation in a thread. The simulation part is in the simrocketenv file that is using the OpenAI gym / Gymnasium interface and using pybullet in the background for the heavy lifting of the physics simulation incl. collision detection. The ctrl_thread_func will either call the MPCPolicy.py OR the NNPolicy.py. So either the rocket is controlled by a model predictive control algorithm or a neural network.

┌───────────────────┐
│  rocketcraft.py   │
│  --------------   │   'state' ┌─────────────────────┐    ┌─────────────────┐
│                   │◄──────────│  simrocketenv.py    │    │ pybullet        │
│  main()           │           │  ---------------    │──► │ --------        │
│                   │   'u'     │                     │    │                 │
│                   │──────────►│  OpenAI gym env.    │    │ Physics engine  │
└───────┬───────────┘           │  Physics simulation │    │ and GUI         │
        │      ▲                └─────────────────────┘    └─────────────────┘
        │      │
'state' │      │ 'u'
        │      │
        ▼      │
┌───────────────────┐
│                   │
│ Controller Thread │ 'state' >
│ ctrl_thread_func()│ < 'u'  ┌─────────────────┐       ┌─────────────────┐
│                   │◄───┬──►│ MPCPolicy.py    │◄────► │ rocketmodel.py  │
│                   │    │   │ --------------  │       │ --------------  │
└───────────────────┘    │   │                 │       │                 │
                       or│   │ NMPC controller │       │ NMPC model and  │
                         │   │ u = next(state) │       │ dynamics        │
                         │   └─────────────────┘       └───┬─────────────┘
                         │   ┌─────────────────┐           │   ┌────────────────┐
                         └──►│ NNPolicy.py     │           └─► │ acados         │
                             │ --------------  │               │ ------         │
                             │                 │               │                │
                             │ Neural network  │               │ Auto generated │
                             │ u = next(state) │               │ C-code         │
                             └─────────────────┘               └────────────────┘

Neural Network and Model Predictive Control

Different control policies are available:

• NNPolicy
• MPCPolicy

Switch between the policies in rocketcraft.py:

# policy = MPCPolicy(initial_state)
policy = NNPolicy()

Run:

python3 src/expert_collect.py

This will write a expert_data.json file with training data (state vector, control input (u) pairs, etc.). Then a new policy can be trained with this data:

python3 src/expert_train.py

This will train a neural network based on the MPC data and generate a torch_nn_mpc-rocket-vX.pth file that can be used by the NNPolicy class.

Model Predictive Control

The core "magic" of the model predictive control is located in the src/mpc/rocket_model.py file. Here the system dynamics are being described. The heavy lifting of solving the MPC problem is performed by the awesome acados library.

Coordinate Frames

pybullet is using:

• World Frame (enu) East/North/Up(ENU): X = East, Y = North, Z = Up
• Body Frame (rosbody), X = Forward, Y = Left, Z = Up

Info

2023-2024 Jan Zwiener. Free to use for academic research if this work is cited and linked. Contact author for commercial use.