SwarmSim

Simulate 2D and 3D swarms of mobile agents with various dynamics and implement distributed control laws to obtain different behaviours.

To execute your first simulation simply run Launcher.m.

Features

• Simulation of swarms of mobile agents (see Launcher.m).
• Implement your own dynamical model or use one of the embedded ones (see integrateAgents.m).
• Implement your own distributed control law or use one of the default ones (see globalInteractionForce.m).
• Acquisition of metrics to evaluate the performance.
• Extensive simulations to study stochastic effects or different initial conditions (see MultiLauncher.m).
• Extensive simulations to study the effects of the parameters (see SequentialLauncher.m).
• Simulate swarms of photo-sensistive microorganisms (see LauncherMicroorg.m)
• Analize DOME experiments, generate digital twins of microorganisms and run virtual experiments (see DOME folder)
• Local stability analysis of lattice configurations via linearization (see CrystalStability.m).
• Interface with Robotarium code to perform advanced simulations and real life experiments (see RobotariumSimulator.m). For more details refer to https://www.robotarium.gatech.edu.
• Plots to visualise the simulation and the metrics.
• Automatic save the results of the simulations.

Versions & Releases

Copyrights: If you use this code for research purposes and want to mention it in one of your publications, please cite [Giusti2023B].

• V3.0 - Authors: Andrea Giusti and Davide Salzano. Date: 2025
• V2.0 - Authors: Andrea Giusti. Date: 2023
• V1.1 - Authors: Andrea Giusti and Gian Carlo Maffettone. Date: 2023
• V1.0 - Authors: Andrea Giusti and Gian Carlo Maffettone. Date: 2022

External resources

The DOME is an open-source platform for the control of microscale agents using light. To use the DOME, perform experiments and analyze the resulting data use DOME-software.

The Robotarium allows to remotely conduct real-life swarm robotics experiments.

For additional information contact andrea.giusti@unina.it.

References

This project implements the algorithms described in [Giusti2023B] (see SwarmSimV1 ), [Giusti2023A] and [Giusti2025]. In the Media folder, there is a video supplement for [Giusti2023B].

• [Giusti2025] Giusti, A., Salzano, D., di Bernardo, M., & Gorochowski, T.E. (2025) Data-driven inference of digital twins for high-throughput phenotyping of motile and light-responsive microorganisms. BioRXiv preprint.
• [Giusti2024] Giusti, A. (2024) Modelling and Control of Spatial Behaviours in Multi-Agent Systems with Applications to Biology and Robotics. PhD Thesis.
• [Giusti2023B] Giusti, A., Maffettone, G. C., Fiore, D., Coraggio, M., & di Bernardo, M. (2023). Distributed Control for Geometric Pattern Formation of Large-Scale Multirobot Systems. Frontiers in Robotics and AI. DOI: 10.3389/frobt.2023.1219931.
• [Giusti2023A] Giusti, A., Coraggio, M., & di Bernardo, M. (2023). Local Convergence of Multi-Agent Systems Toward Rigid Lattices. IEEE Control Systems Letters. DOI:10.1109/LCSYS.2023.3289060.