AI 中文总结
本文针对低空智能体的三维多无人机路径规划问题，提出FORTUNE分层离线-在线框架，其在真实与合成场景中性能优于现有最优方法。
AI 中文摘要
低空智能体的普及提升了动态城市环境中及时且符合社会规范的协同感知需求。然而，同时应对异构时空需求、环境不确定性以及以人为中心的操作约束仍是挑战。本文研究地面兴趣点（PoI）需求不确定下的三维多无人机（UAV）路径规划与任务分配问题。与现有假设PoI静态且完全已知的研究不同，我们在统一框架内对持续、时间可预测及突发的需求进行建模。我们进一步纳入与高度相关的社会和环境成本，包括噪声暴露与公共安全风险，以平衡感知性能与符合社会规范的操作。为求解所得大规模混合整数非线性问题，我们提出FORTUNE，一种分层的离线-在线框架。离线阶段，Transformer预测II型PoI激活窗口，而增强型麻雀搜索算法通过优先级感知解码与危险感知进化生成协同飞行计划；在线阶段，轻量细化模块在保持全局任务一致性的同时适配突发的III型PoI。基于真实交通数据与合成场景的实验表明，FORTUNE在有效性、可扩展性及实际适用性上始终优于现有最优方法。
英文摘要
The proliferation of low-altitude intelligent agents is increasing the demand for timely and socially responsible collaborative sensing in dynamic urban environments. However, jointly addressing heterogeneous spatiotemporal demands, environmental uncertainty, and human-centered operational constraints remains challenging. This paper studies 3D multi-UAV path planning and task assignment under uncertain ground PoI demands. Unlike existing work assuming static and fully known PoIs, we model persistent, temporally predictable, and emergent demands within a unified framework. We further incorporate altitude-dependent societal and environmental costs, including noise exposure and public safety risks, to balance sensing performance with socially compliant operations. To solve the resulting large-scale mixed-integer nonlinear problem, we propose FORTUNE, a hierarchical offline-online framework. Offline, a Transformer predicts Type-II PoI activation windows, while an enhanced sparrow search algorithm generates coordinated flight plans through priority-aware decoding and danger-aware evolution. Online, a lightweight refinement module accommodates emerging Type-III PoIs while preserving global mission coherence. Experiments on real-world traffic data and synthetic scenarios show that FORTUNE consistently outperforms state-of-the-art methods in effectiveness, scalability, and practical applicability.
