GELATO: Generic Launch Trajectory Optimizer

GELATO is an open source tool for launch trajectory optimization, written in Python.

GELATO solves trajectory optimization problems using the Legendre-Gauss-Radau pseudospectral method, which is stable and robust for highly complex nonlinear problems.

Features

• Maximization of payload mass by changing the angular rate profile and event times (such as cutoff time)
• 3DoF calculation of dynamics with thrust, aerodynamics and gravity
• Support for multi-stage launch vehicles
• Automatic computation of Jacobians using the CasADi framework

Install

This program uses the libraries below:

• NumPy
• SciPy
• CasADi
• Pandas
• Simplekml (Optional: for tools/make_kml.py only)
• Matplotlib (Optional: for tools/plot_output.py only)
• Flask (Optional: for tools/settings_editor.py only)

The IPOPT solver for NLP is included with CasADi. If you wish to use SNOPT as the solver, please set the environment variables appropriately. For Linux, add the location of libsnopt7_cpp.so to LD_LIBRARY_PATH.

Usage

Prepare input files in the root directory of the repository.

• Setting JSON file
• Related setting files (initial trajectory, wind, aerodynamics coefficients, etc.)

You can also create and edit input files using the GUI by running tools/settings_editor.py.

Run

make
python3 Trajectory_Optimization.py [setting file name]

See the example folder for an example set of input files.

References

• David, Benson. (2005). A Gauss Pseudospectral Transcription for Optimal Control.
• Garrido, José. (2021). Development of Multiphase Radau Pseudospectral Method for Optimal Control Problems.
• Di Campli Bayard de Volo, G. (2017). Vega Launchers' Trajectory Optimization Using a Pseudospectral Transcription.
• Garg, Divya & Patterson, Michael & Hager, William & Rao, Anil & Benson, David & Huntington, Geoffrey. (2009). An overview of three pseudospectral methods for the numerical solution of optimal control problems. Advances in the Astronautical Sciences. 135.