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This paper introduces FORTUNE, a hierarchical framework for 3D multi-UAV path planning that addresses the complexities of low-altitude operations in dynamic urban environments by modeling uncertain ground point-of-interest (PoI) demands. By integrating altitude-dependent societal costs and environmental risks, the method balances operational efficiency with public safety and compliance. Experimental results demonstrate that FORTUNE significantly outperforms existing approaches in terms of effectiveness and scalability while maintaining practical applicability in real-world scenarios.
FORTUNE redefines UAV path planning by effectively managing uncertain PoI demands while prioritizing public safety and operational efficiency.
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.
