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This toolkit addresses the critical need for scalable simulation environments in robotics by integrating ROS 2 with Unity, enabling mixed-reality experiments that bridge virtual and physical sensor data. By facilitating distributed fleet management tasks such as task assignment, it provides a practical framework for validating autonomous mobile robot algorithms before real-world deployment. The availability of containerized routines lowers the barrier to entry for complex multi-robot coordination research.
VirTooS: A ROS 2 - Unity Virtualization Toolkit for Fleet Management of Autonomous Mobile Robots
arXiv:2608.26066v1 Announce Type: new 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.
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.
Robotics (cs.RO)
arXiv:2608.26066 [cs.RO]
(or arXiv:2608.26066v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.26066 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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