Accompanying code for the paper Learning barrier-certified polynomial dynamical systems for obstacle avoidance with robots by Martin Schonger^1*, Hugo T. M. Kussaba^1*, Lingyun Chen¹, Luis Figueredo², Abdalla Swikir¹, Aude Billard³, and Sami Haddadin¹, which has been submitted to ICRA 2024 and is currently under review.

¹Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich (TUM), Germany. Abdalla Swikir is also with the Department of Electrical and Electronic Engineering, Omar Al-Mukhtar University (OMU), Albaida, Libya.
²School of Computer Science, University of Nottingham, UK. Luis Figueredo is also an Associated Fellow at the MIRMI, TUM.
³Learning Algorithms and Systems Laboratory, EPFL, Switzerland.
^*These authors contributed equally to the paper.

Install MATLAB (tested with R2023a).

Install the required MathWorks toolboxes: Control System Toolbox, Robust Control Toolbox, Optimization Toolbox, Signal Processing Toolbox, Symbolic Math Toolbox, Statistics and Machine Learning Toolbox.

Install the required third party tools: YALMIP, PENLAB+PENBMI, MOSEK, GUROBI, (Optional for plotting: crameri colormaps).

Note Make sure that the non-toolbox paths are before/on top of the toolbox paths.

Run:

git clone https://github.com/martinschonger/abc-ds.git
cd abc-ds
git submodule init
git submodule update

Open the abc-ds folder in MATLAB.

Configure the desired experiments in main2.m and run this script.

Check the output folder for results and logs.

(Optionally, recreate the plots from the paper with generate_plots.m, and the animations from the video with generate_plots_video.m.)

[email protected]

This software was created as part of Martin Schonger's master's thesis in Computer Science at the Technical University of Munich's (TUM) School of Computation, Information and Technology (CIT).

Copyright © 2023 Martin Schonger
This software is licensed under the GPLv3.