SkyRover：跨域路径规划的模块化模拟器
SkyRover: A Modular Simulator for Cross-Domain Pathfinding
AI总结：
针对现有模拟器局限于单领域的问题，本文提出模块化模拟器SkyRover，支持UAV-AGV多智能体路径规划的真实动力学、可配置3D环境及便捷API，助力跨域算法设计测试与基准测试，实验验证其高效性与高保真度。
摘要：
无人驾驶飞行器（UAV）和自动导引车（AGV）在物流、监视、巡检等任务中的协作日益增多，但现有模拟器常聚焦单一领域，限制了跨域研究。本文提出SkyRover，一款用于UAV-AGV多智能体路径规划（MAPF）的模块化模拟器，支持真实智能体动力学、可配置3D环境，以及便于外部求解器和学习方法接入的API。通过统一地面与空中操作，它助力跨域算法的设计、测试与基准测试，实验证明其在UAV-AGV协同中具备高效路径规划与高保真模拟能力。项目地址：https://sites.google.com/view/mapf3d/home。
英文摘要：
Unmanned Aerial Vehicles (UAVs) and Automated Guided Vehicles (AGVs) increasingly collaborate in logistics, surveillance, inspection tasks and etc. However, existing simulators often focus on a single domain, limiting cross-domain study. This paper presents the SkyRover, a modular simulator for UAV-AGV multi-agent pathfinding (MAPF). SkyRover supports realistic agent dynamics, configurable 3D environments, and convenient APIs for external solvers and learning methods. By unifying ground and aerial operations, it facilitates cross-domain algorithm design, testing, and benchmarking. Experiments highlight SkyRover's capacity for efficient pathfinding and high-fidelity simulations in UAV-AGV coordination. Project is available at https://sites.google.com/view/mapf3d/home.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
