SkyRover: A Modular Simulator for Cross-Domain Pathfinding

Abstract

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. We believe the SkyRover fills a key gap in MAPF research. Project is available at https://sites.google.com/view/mapf3d/home.

Cite

Text

Ma et al. "SkyRover: A Modular Simulator for Cross-Domain Pathfinding." International Joint Conference on Artificial Intelligence, 2025. doi:10.24963/IJCAI.2025/1268

Markdown

[Ma et al. "SkyRover: A Modular Simulator for Cross-Domain Pathfinding." International Joint Conference on Artificial Intelligence, 2025.](https://mlanthology.org/ijcai/2025/ma2025ijcai-skyrover/) doi:10.24963/IJCAI.2025/1268

BibTeX

@inproceedings{ma2025ijcai-skyrover,
  title     = {{SkyRover: A Modular Simulator for Cross-Domain Pathfinding}},
  author    = {Ma, Wenhui and Li, Wenhao and Jin, Bo and Lu, Changhong and Wang, Xiangfeng},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2025},
  pages     = {11086-11090},
  doi       = {10.24963/IJCAI.2025/1268},
  url       = {https://mlanthology.org/ijcai/2025/ma2025ijcai-skyrover/}
}