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.

翻译：随着空间智能的不断发展，异构多智能体系统——特别是无人机（UAV）与无人地面车辆（UGV）之间的协作——在搜索救援、城市监控和环境监测等复杂应用中展现出巨大潜力。然而，现有仿真平台主要针对单智能体动力学设计，缺乏用于交互式空地协同仿真的专用框架。本文提出AirSimAG，一个基于高度定制化AirSim框架构建的高保真空地协同仿真平台。该平台支持多智能体同步仿真，并兼容无人机-无人地面车辆系统的异构感知与控制接口。为展示其能力，我们设计了一系列具有代表性的空地协同任务，包括地图构建、路径规划、目标跟踪、队形控制及自主探索。我们进一步基于这些任务提供定量分析，以说明该平台在支持多智能体协调与跨模态数据一致性方面的有效性。AirSimAG仿真平台现已开源发布于https://github.com/BIULab-BUAA/AirSimAG。