AirSimAG: A High-Fidelity Simulation Platform for Air-Ground Collaborative Robotics
This work addresses the need for simulation tools for researchers and developers working on heterogeneous multi-agent systems in applications such as search and rescue and urban surveillance, though it is incremental as it builds upon an existing framework.
The paper tackles the lack of dedicated simulation platforms for interactive air-ground collaborative robotics by presenting AirSimAG, a high-fidelity platform built on a customized AirSim framework, which enables synchronized multi-agent simulation and supports heterogeneous sensing and control interfaces for UAV-UGV systems, with quantitative analyses demonstrating its effectiveness in tasks like mapping and planning.
As spatial intelligence continues to evolve, heterogeneous multi-agent systems-particularly the collaboration between Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs), have demonstrated strong potential in complex applications such as search and rescue, urban surveillance, and environmental monitoring. However, existing simulation platforms are primarily designed for single-agent dynamics and lack dedicated frameworks for interactive air-ground collaborative simulation. In this paper, we present AirsimAG, a high-fidelity air-ground collaborative simulation platform built upon an extensively customized AirSim framework. The platform enables synchronized multi-agent simulation and supports heterogeneous sensing and control interfaces for UAV-UGV systems. To demonstrate its capabilities, we design a set of representative air-ground collaborative tasks, including mapping, planning, tracking, formation, and exploration. We further provide quantitative analyses based on these tasks to illustrate the platform effectiveness in supporting multi-agent coordination and cross-modal data consistency. The AirsimAG simulation platform is publicly available at https://github.com/BIULab-BUAA/AirSimAG.