打造可跨空地协同的多智能体路径规划仿真平台
SkyRover: A Modular Simulator for Cross-Domain Pathfinding
- 模块化设计支持无人机与自动导引车协同路径规划
- 真实动态模拟与3D环境配置，实现高效路径求解
- 适合研究空地协同算法的研究者和开发者
无人机（UAV）与自动导引车（AGV）在物流、监控、巡检等任务中日益协同工作。然而，现有仿真平台多聚焦单一领域，限制了跨域研究。本文提出SkyRover，一个面向无人机-自动导引车多智能体路径规划（MAPF）的模块化仿真器。SkyRover支持真实的智能体动力学、可配置的3D环境，并提供便捷的外部求解器与学习方法接口。通过统一地面与空中作业，它促进了跨域算法的设计、测试与基准评估。实验表明，SkyRover在无人机-自动导引车协同路径规划中具备高效的路径求解能力与高保真度仿真性能。项目主页：https://sites.google.com/view/mapf3d/home。
原文摘要 · Abstract (English)
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.
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