multirobotsimulation

Multirobot simulation refers to computational environments that model multiple autonomous robots operating within a shared virtual world to study cooperative behaviors, coordination algorithms, and system performance. It supports research in robotics, artificial intelligence, control theory, and human–robot interaction by enabling experimentation that may be impractical with real hardware alone.

A multirobot simulation typically includes a physics engine to simulate dynamics, detailed robot models with actuators and sensors, and models of communication networks, including delays and packet loss. The environment provides obstacles, terrain, and interaction with objects. Time can be simulated in real time or accelerated, and simulations may support hardware-in-the-loop or digital twins for closer alignment with real hardware.

Platforms and approaches vary. Some simulations emphasize centralized planning or centralized state estimation, while others focus on decentralized or swarm-based control. Popular tools and ecosystems include Gazebo, Webots, MORSE, Stage, ARGoS, and CoppeliaSim, often integrated with ROS or ROS 2. These platforms differ in scalability, fidelity, and ease of modeling heterogeneous robot teams, and are used for tasks such as multi-robot exploration, mapping, task allocation, formation control, and cooperative manipulation.

Key evaluation metrics include scalability to large robot ensembles, fidelity of sensor and actuator models, reproducibility of results, communication load, energy usage, and the quality of emergent collective behaviors. Challenges include the sim-to-real gap, realistic communication modeling, heterogeneous hardware support, time synchronization, and maintaining performance as the robot count grows. Ongoing work explores domain randomization, hardware-in-the-loop, cloud-based simulations, and digital twins to bridge simulation and real-world deployment.