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 30m×30m 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=25s . The red robot represents the virtual twin of the physical Jackal.
Fig. 11: Snapshots of the real ( Robot1,R ) and virtual ( Roboti,V ) robots’ trajectories during the mixed-reality experiment at different time instants. The square marker ( □ ) represents the initial robot position.
ORCESTRA is a mixed-reality system for programming robot digital twins through no-code waypoint teaching and language-guided control. In a passthrough mixed-reality workspace, users place robot twins on real surfaces, teach trajectories, save robot-relative episodes, or issue spoken/typed commands that a vision-language model converts into structured digital-twin plans. Both interaction modes share a backend for metric grounding, embodiment-aware validation, preview, confirmation, and digital-twin execution. The system supports heterogeneous robot embodiments, including fixed-base manipulators, a mobile base, and a humanoid robot, demonstrating MR validation as a safety layer for language-guided robot programming before physical deployment.
Ivan Snegirev, Elizaveta Semenyakina, Mikhail Konenkov +3
Intelligent Space Robotics Lab, Skolkovo Institute of Science and Technology, Moscow, Russian Federation · R&D Center, MWS, Moscow, Russian Federation
This paper presents a Vehicle-to-Everything (V2X) communication framework that enables decentralized cooperation among social robots operating in complex urban traffic environments. Building on ETSI Cooperative Awareness and Maneuver Coordination services, the framework introduces two robot-centric facility-layer services: the Robot Awareness Service (RAS) and the Robot Maneuver Coordination Service (RMCS), realized through the Robot Awareness Message (RAM) and the Robot Maneuver Coordination Message (RMCM), respectively. RAS enables role-aware, task-oriented robot awareness while integrating externally detected Vulnerable Road Users (VRUs), including non-V2X pedestrians, into cooperative awareness. RMCS supports event-driven, low-latency coordination of robot maneuvers under explicitly established roles, without centralized infrastructure or prior pairing. A real-world proof of concept demonstrates deterministic multi-robot coordination between a humanoid robot and a quadrupedal robot assisting a pedestrian during a road-crossing scenario, governed by a formally specified finite-state coordination model. Complementary simulations evaluate robot-mediated VRU clustering in mixed V2X environments, showing that RAS-based clustering integrates non-V2X VRUs in safety-critical areas while reducing redundant transmissions from V2X-enabled VRUs, thereby lowering channel load. Together, the proposed services provide a scalable and standards-aligned foundation for integrating cooperative robots into future Connected, Cooperative, and Automated Mobility ecosystems.
John Pravin Arockiasamy, Alexey Vinel
*Karlsruhe Institute of Technology, Karlsruhe, Germany · †Halmstad University, Halmstad, Sweden
ROScopter is a lean multirotor autopilot built for researchers. ROScopter seeks to accelerate simulation and hardware testing of research code with an architecture that is both easy to understand and simple to modify. ROScopter is designed to interface with ROSflight 2.0 and runs entirely on an onboard flight computer, leveraging the features of ROS 2 to improve modularity. This work describes the architecture of ROScopter and how it can be used to test application code in both simulated and hardware environments. Hardware results of the default ROScopter behavior are presented, showing that ROScopter achieves similar performance to another state-of-the-art autopilot for basic waypoint-following maneuvers, but with a significantly reduced and more modular code-base.