SCPToolbox.jl

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Sequential Convex Programming Toolbox for nonconvex trajectory optimization.

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- - CI.yml
- - .gitignore
  - about.md
  - logo.png
  - main.tex
  - scp.svg
- - .gitkeep
- - argument.jl
  - block.jl
  - cone.jl
  - constraint.jl
  - cost.jl
  - function.jl
  - general.jl
  - parser.jl
  - perturbation.jl
  - printing.jl
  - problem.jl
  - program.jl
  - scaling.jl
  - variation.jl
- - discretization.jl
  - general.jl
  - gusto.jl
  - ptr.jl
  - scp.jl
  - scvx.jl
  - solvers.jl
- - basic_types.jl
  - dltv.jl
  - ellipsoid.jl
  - globals.jl
  - helper.jl
  - homotopy.jl
  - hyperrectangle.jl
  - plots.jl
  - quaternion.jl
  - scene_3d.jl
  - table.jl
  - trajectory.jl
  - tree.jl
  - utils.jl
- SCPToolbox.jl
- - examples.jl
- runtests.jl
.gitignore
LICENSE.txt
Project.toml
README.md

// repository documentation

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> :information_source: The repository name has changed to `SCPToolbox.jl` in
> order to reflect the project's direction: to develop a general-purpose
> trajectory optimization toolkit using sequential convex programming
> algorithms.

The SCP Toolbox provides the tools necessary to define and solve
nonconvex trajectory optimization problems. The user-facing part of the toolbox
provides a trajectory problem parser that allows one to define the system
dynamics, state and input constraints, and boundary conditions. Under the hood,
the problem is solved using any one of several _Sequential Convex Programming_
(SCP) algorithms. These algorithms have been successfully demonstrated on a
number of difficult aerospace, autonomous driving, robotics, and other
applications. A major goal of the SCP Toolbox is to provide working reference
implementations of the SCP algorithms. By placing the algorithms behind a
parser that transforms trajectory problems into their abstract mathematical
definitions, the algorithms can be generically tested on a suite of examples
without having to re-implement the underlying algorithms each time.

## Getting Started

Click on the [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/UW-ACL/SCPToolbox_tutorial/master?labpath=tutorial%2Fsrc%2Fp1_clp.ipynb) button to spin up a remote Jupyter environment. Follow the included notebooks to get a feel for the toolbox, and finish by solving a self-guided tutorial to land a rocket on the Moon! Check out the [SCPToolbox_tutorial](https://github.com/UW-ACL/SCPToolbox_tutorial) repository for more details.

## Implemented SCP algorithms

The following algorithms are implemented, and can be found in the
`src/solvers/` directory:

- Penalized trust region ([PTR](https://arxiv.org/abs/1811.10803))
- Successive convexification ([SCvx](https://arxiv.org/abs/1804.06539))
- Guaranteed Sequential Trajectory Optimization ([GuSTO](http://asl.stanford.edu/wp-content/papercite-data/pdf/Bonalli.Cauligi.Bylard.Pavone.ICRA19.pdf))
- Lossless convexification ([LCvx](https://doi.org/10.2514/1.27553))

## Implemented examples

Several example applications show how the algorithms can be used. These can all
be found in the `test/examples/` directory, and include:

1. [Double integrator with friction](test/examples/double_integrator)
2. [Mars rocket landing](test/examples/rocket_landing)
3. [SpaceX Starship landing "flip" maneuver](test/examples/starship_flip)
4. [Mass-spring-damper with an actuator deadband or
   "sticking"](test/examples/oscillator)
5. [Quadrotor flight around obstacles](test/examples/quadrotor)
6. [Space station freeflyer robot](test/examples/freeflyer)
7. [Planar spacecraft rendezvous with discrete
   logic](test/examples/rendezvous_planar)
8. [Apollo transposition and docking maneuver with discrete
   logic](test/examples/rendezvous_3d)

## Citing

If you use the SCP Toolbox, kindly cite the following associated publication.

```
@article{SCPToolboxCSM2022,
  doi = {10.1109/mcs.2022.3187542},
  url = {https://doi.org/10.1109/mcs.2022.3187542},
  year = {2022},
  month = oct,
  publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
  volume = {42},
  number = {5},
  pages = {40--113},
  author = {Danylo Malyuta and Taylor P. Reynolds and Michael Szmuk and Thomas Lew
            and Riccardo Bonalli and Marco Pavone and Behçet Açıkmeşe},
  title = {Convex Optimization for Trajectory Generation: A Tutorial on Generating
           Dynamically Feasible Trajectories Reliably and Efficiently},
  journal = {{IEEE} Control Systems},
  note = {Free preprint available at https://arxiv.org/abs/2106.09125}
}
```

SCPToolbox.jl

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