2023-code-ACC-Distributed Model Predictive Flocking with Obstacle Avoidance and Asymmetric Interaction Forces
This repository contains an implementation of the algorithms and simulations presented in
P.Hastedt and H. Werner, "Distributed Model Predictive Flocking with Obstacle Avoidance and Asymmetric Interaction Forces"
It may be used to recreate and validate the simulation results and figures from the paper. To do so, run either of the two scripts simulation.m and evaluation.m.
Additionally, videos for the scenarios described in the paper are provided in the videos directory.
Running the simulations can take up to 10 minutes depending on the computer hardware.
For the simulations, an open source MAS library which can be found on GitHub is utilized.
At the top of simulation.m, the algorithm and scenario to be simulated can be selected by changing the algorithmIndex and scenarioIndex variables. The simulation results will be saved in the simulation/out directory and can then be used for evaluation.
At the top of evaluation.m, the scenarios to be compared can be selected by changing the scenarioIndex variable. To evaluate additional data generated by the simulation, copy the .mat files from the simulation/out directory to the data directory and add the name of the data file to the simData array at the top of evaluation.m.
When downloading the code from Zenodo, the MAS-simulation submodule directory simulation/MAS-simulation will be empty. This can be resolved by either directly downloading the code for the paper from GitHub or by copying the source code of the MAS library to the corresponding directory.
The code in this repository was tested in the following environment:
- Windows 10 Version 21H2
- Matlab 2021a