cs.ROAug 26, 2026

VirTooS: A ROS 2 - Unity Virtualization Toolkit for Fleet Management of Autonomous Mobile Robots

Authors: Andrea Drudi, Lorenzo Pichierri, Andrea Testa, Giuseppe Notarstefano

Organizations: Department of Electrical, Electronic and Information Engineering, University of Bologna, Bologna, Italy.

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Abstract

In this paper, we present VirTooS, a Python/C# toolkit designed to implement fleet-management tasks on teams of Autonomous Mobile Robots (AMRs). VirTooS leverages the Robot Operating System (ROS) 2 and Unity game engine to provide realistic, scalable virtual experiments in a mixed-reality environment. The toolbox allows users to easily generate and customize virtual scenarios for realistic simulations. Virtual and real sensors as, e.g., LiDARs, can be exploited to map and safely navigate in the mixed-reality environment. To enable distributed robotics experiments, we propose a set of tailored routines leveraging the ChoiRbot framework. As a motivating example, we show a set of experiments for task assignment problems in a virtual environment, allowing seamless interaction among real and virtual robots. Moreover, the package comes with a containerized suite to easily deploy it on different machines. The source code will be made publicly available on GitHub.

Figures & tables

Fig. 1: Software architecture of VirTooS for cooperative robotics in complex indoor environments.
Fig. 2: Graphical representation of the communication scheme between Unity and ROS 2.
Fig. 3: Snapshot of the Unity interface for the dynamic generation of virtual environments. The last menu (i.e., Robot ) allows the user to personalize the robot settings to be used in the simulation.
Fig. 4: Example of an environment dynamically generated in Unity. In this setup, 50 Jackal ground robots are navigating in a 30 m×30 m30\,\mathrm{m}\times 30\,\mathrm{m} virtual indoor environment.
Fig. 5: Snapshot of the Unity interface for the configuration of the laser scanner parameters.
Fig. 6: Examples of Task Assignment experiments with robots navigating in two different settings: on the left, randomly placed shelves, while on the right, shelves placed in racks.
Fig. 7: A Jackal mobile robot navigating in a virtual environment generated in Unity (left) while mapping the surroundings (right).
Fig. 8: A side-by-side view of a physical Jackal in our laboratory (left) and the virtual twin in the Unity-based simulation (right) navigating in the environment.
Fig. 9: A snapshot from the mixed-reality environment. The red robot is the virtual twin of the real Jackal, while the green robots are the fully virtual robots.
Fig. 10: Snapshot of the mixed-reality experiment at t=25 st=25\,\mathrm{s} . The red robot represents the virtual twin of the physical Jackal.
Fig. 11: Snapshots of the real ( Robot1,RRobot_{1,R} ) and virtual ( Roboti,VRobot_{i,V} ) robots’ trajectories during the mixed-reality experiment at different time instants. The square marker ( □\square ) represents the initial robot position.
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