Sequential convex programming (SCP) enables the real-time generation of dynamically feasible trajectories for robotic, aerospace, and other systems. Under the hood, the algorithm relies on optimal control and convex optimization theory.
This project contains the code for several SCP algorithms, and several examples
of using these algorithms for trajectory generation. The examples can be found
in the examples/ directory, and include:
- Quadrotor path planning with obstacle avoidance
- Freeflyer flight inside of a space station environment with obstacles
- SpaceX Starship landing "flip" maneuver
- Variable-mass rocket landing using lossless convexification. This does not rely on SCP, but rather on pure convex optimization.
More documentation is on its way 📚
If you use this code, kindly cite the following associated publication.
@article{SCPTrajOptCSM2021,
year = {2021},
publisher = {{IEEE}},
author = {Danylo Malyuta and Taylor P. Reynolds and Michael Szmuk
and Thomas Lew and Riccardo Bonalli and Marco Pavone
and Behcet Acikmese},
title = {Convex Optimization for Trajectory Generation},
journal = {{IEEE} Control Systems Magazine (in review)},
pages = {arXiv:2106.09125}
}