HERCULES是支持空地协同的开源仿真平台，用于多机器人自主导航与感知。
HERCULES: An Open-Source Simulation Framework for Heterogeneous Multi-Robot SLAM, Collaborative Perception, and Exploration
- 基于UE5构建，支持无人机与地面车同步运行的动态仿真
- 集成红外相机与夜视模式，支持恶劣视觉环境下的多模态数据采集
- 提供轻量API与ROS 2接口，适合研究多机器人协同探索与SLAM
我们提出HERCULES，一个面向异构多机器人自主系统的开源仿真框架与数据采集管道。基于Unreal Engine 5（UE5）的AirSim与Cosys-AirSim，HERCULES克服了先前框架的架构局限，实现在大规模、逼真动态环境中的无人机-地面车（UAV-UGV）并发操作。引入新型航点追踪式地面车控制器，兼容现有无人机控制接口，并提供跨平台共享导航栈，涵盖建图、可通行性分析、路径规划与控制。扩展传感器套件，新增基于物理的长波红外（LWIR）摄像头和可配置夜视模式，以应对视觉退化环境。提供轻量级API、ROS 2封装及严格的时间同步机制，将前沿游戏引擎能力融入机器人仿真，集成行人、交通、野生动物等智能体，以及火灾、洪水、作物病害传播等高保真动态现象。系统支持两种运行模式：被动模式下重放离线设计轨迹生成可复现的多模态数据集；主动模式下通过在线规划闭环执行。在异构多机器人SLAM、协同感知与探索任务中，基于HERCULES生成的数据与主动闭环执行的实验验证了其有效性。我们公开发布源代码、实验代码、文档及数据集，包括在千米级沙漠、森林与城市环境中由两架无人机与两辆地面车采集的异构多机器人SLAM基准数据集，详见https://lunarlab-gatech.github.io/HERCULES-website。
原文摘要 · Abstract (English)
We present HERCULES, an open-source simulator and data-collection pipeline for heterogeneous multi-robot autonomy. Built upon the Unreal Engine 5 (UE5)-based simulators AirSim and Cosys-AirSim, HERCULES resolves key architectural limitations of prior frameworks to enable concurrent unmanned aerial and ground vehicle (UAV-UGV) operation in large-scale, photorealistic, dynamic environments. It introduces a new waypoint-tracking UGV controller that mirrors existing UAV control interfaces, and provides a shared navigation stack for mapping, traversability analysis, planning, and control across heterogeneous platforms. Expanding inherited sensor suites, it adds physics-based long-wave infrared (LWIR) cameras and configurable night-vision modes for degraded visual environments. HERCULES provides lightweight APIs, ROS 2 wrappers, and rigorous time synchronization across sensors and platforms, and brings state-of-the-art game-engine capabilities into robotics simulation, integrating intelligent agents such as pedestrians, traffic, and wildlife with high-fidelity dynamic phenomena, including fire, flooding, and crop disease spread. HERCULES runs in two modes: passively, replaying offline-designed trajectories to generate reproducible multi-modal datasets, and actively, running an online planner in closed loop from live observations. Our experiments in heterogeneous multi-robot SLAM, collaborative perception, and exploration, using both HERCULES-generated data and active closed-loop execution, demonstrate its utility for advancing heterogeneous multi-robot autonomy. We publicly release our source code, experiment code, documentation, and datasets, including a heterogeneous multi-robot SLAM benchmark collected with two UAVs and two UGVs across kilometer-scale desert, forest, and city environments, at https://lunarlab-gatech.github.io/HERCULES-website.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。