{"type":"ScholarlyArticle","title":"SkyRover: A Modular Simulator for Cross-Domain Pathfinding","authors":[{"name":"Wenhui Ma","slug":"wenhui-ma","given":"Wenhui","family":"Ma","affiliation":""},{"name":"Wenhao Li","slug":"wenhao-li","given":"Wenhao","family":"Li","affiliation":""},{"name":"Bo Jin","slug":"bo-jin","given":"Bo","family":"Jin","affiliation":""},{"name":"Changhong Lu","slug":"changhong-lu","given":"Changhong","family":"Lu","affiliation":""},{"name":"Xiangfeng Wang","slug":"xiangfeng-wang","given":"Xiangfeng","family":"Wang","affiliation":""}],"abstract":"Unmanned Aerial Vehicles (UAVs) and Automated Guided Vehicles (AGVs) increasingly collaborate in logistics, surveillance, inspection tasks and etc. \r\nHowever, existing simulators often focus on a single domain, limiting cross-domain study. \r\nThis paper presents the SkyRover, a modular simulator for UAV-AGV multi-agent pathfinding (MAPF). \r\nSkyRover supports realistic agent dynamics, configurable 3D environments, and convenient APIs for external solvers and learning methods. \r\nBy unifying ground and aerial operations, it facilitates cross-domain algorithm design, testing, and benchmarking. \r\nExperiments highlight SkyRover’s capacity for efficient pathfinding and high-fidelity simulations in UAV-AGV coordination.\r\nWe believe the SkyRover fills a key gap in MAPF research. \r\nProject is available at https://sites.google.com/view/mapf3d/home.","doi":"10.24963/ijcai.2025/1268","url":"https://ijcai.org/proceedings/2025/1268","pdf":"https://ijcai.org/proceedings/2025/1268.pdf","markdown":"https://ijcai.org/proceedings/2025/1268.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1268","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Demo Track","track":"Demo Track","number":1268,"pages":{"first":11086,"last":11090},"keywords":["Planning and Scheduling: PS: Search in planning and scheduling","Planning and Scheduling: PS: Learning in planning and scheduling","Robotics: ROB: Motion and path planning","Agent-based and Multi-agent Systems: MAS: Engineering methods, platforms, languages and tools"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}