打造高保真空地协同仿真平台，支持无人机与地面机器人协同作业。
AirSimAG: A High-Fidelity Simulation Platform for Air-Ground Collaborative Robotics
- 基于定制化AirSim构建，支持空地多智能体同步仿真。
- 实现跨模态数据一致性，验证多任务协同有效性。
- 适合研究空地协同的算法开发与系统测试人员。
随着空间智能的发展，异构多智能体系统——特别是无人机（UAV）与无人地面车（UGV）的协作——在搜救、城市监控和环境监测等复杂场景中展现出巨大潜力。然而，现有仿真平台主要针对单智能体动态设计，缺乏专门支持空地交互协同仿真的框架。本文提出AirsimAG，一个基于深度定制AirSim框架的高保真空地协同仿真平台，支持多智能体同步仿真，并提供异构感知与控制接口。为验证其能力，我们设计了包括建图、规划、追踪、编队和探索在内的代表性协同任务，并通过定量分析展示平台在多智能体协调与跨模态数据一致性方面的有效性。AirsimAG已开源，地址：https://github.com/BIULab-BUAA/AirSimAG。
原文摘要 · Abstract (English)
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.
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