solver_test

Examples to test different optimization solvers.

$ git clone https://github.com/hai-zhu/solver_test.git
$ cd solver_test
$ git submodule update --recursive --init
$ cd external/acados
$ mkdir -p build
$ cd build 
$ cmake -DACADOS_WITH_QPOASES=ON ..
$ make install -j4

Testing environment

Ubuntu 18.04
MATLAB R2019b
Python 3.8
Casadi 3.5.1

A valid licence is required if you want to test the Forces Pro solver.

Acados needs to be installed if you want test with it.

MATLAB test

Open a MATLAB instance and navigate the code folder.
Run the "setPath.m" script to add path to MATLAB.
Run the following script to test different solvers:
- yalmip_example.m
- forces_pro_example.m
- casadi_opti_example.m
- casadi_shooting_example.m
- casadi_collocation_example.m

MATLAB ACADOS test

If you want to test the acados solver with MATLAB, first navigate to the directory './matlab_impl/mpc/acados/'. From the folder, open a terminal and run 'source env_set.sh'. Next open MATLAB from the terminal via the command 'matlab'. Then you can run the 'acados_example.m' script to test the acados solver.

Python acados test

Open a terminal and navigate to the repo directory.

$ cd acados_jackal_example/
$ virtualenv .env
$ source .env/bin/activate
$ pip3 install -e ../external/acados/interfaces/acados_template/
$ source env_set.sh
$ python jackal_mpc_example.py

Python Forces_Pro test

Copy the forces_pro_client folder to the forces_jackal_example folder.
Navigate to the forces_jackal_example directory and open a MATLAB instance to generate the solver.
- generate_solver.m
Open a terminal and navigate to the forces_jackal_example directory.

$ python -m venv .env
$ source .env/bin/activate
$ pip install -r ./forces_pro_client/requirements.txt 
$ source env_set.sh
$ python jackal_mpc_example.py

If you find this code useful in your research then please cite:

@article{Zhu2019RAL,
    title = {{Chance-Constrained Collision Avoidance for MAVs in Dynamic Environments}},
    author = {Zhu, Hai and Alonso-Mora, Javier},
    journal = {IEEE Robotics and Automation Letters},
    number = {2},
    volume = {4},
    pages = {776--783},
    publisher = {IEEE},
    year = {2019}
}

@inproceedings{Zhu2020ICRA,
    title = {{Robust Vision-based Obstacle Avoidance for Micro Aerial Vehicles in Dynamic Environments}},
    author = {Lin, Jiahao and Zhu, Hai and Alonso-Mora, Javier},
    booktitle = {2020 IEEE International Conference on Robotics and Automation (ICRA)},
    pages = {2682--2688},
    publisher = {IEEE},
    year = {2020}
}