Problem
Research questions and friction points this paper is trying to address.
3D path planning
multi-UAV coordination
uncertain PoI demands
human-centered constraints
low-altitude operations
Flying over The Uncertain Nature (FORTUNE): Intelligent and Humanistic 3D Path Planning for Low-Altitude Collaboration
🤖 AI Summary
This work addresses the challenges of multi-drone cooperative perception in dynamic urban low-altitude environments, where heterogeneous spatiotemporal demands, environmental uncertainty, and social constraints are difficult to model cohesively. The authors propose FORTUNE, a hierarchical offline-online framework: in the offline phase, a Transformer predicts activation windows for points of interest, and an enhanced sparrow search algorithm—augmented with priority-aware decoding and risk-aware evolutionary mechanisms—generates optimized trajectories; in the online phase, a lightweight module dynamically handles emergent tasks while preserving global consistency. FORTUNE is the first to unify persistent, predictable, and sporadic ground-level sensing demands within a single model, while also integrating societal costs such as noise pollution and public safety. Experimental results demonstrate that FORTUNE significantly outperforms existing approaches in task effectiveness, scalability, and real-world applicability.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
3D multi-UAV path planning
uncertain PoI modeling
societal-aware optimization
hierarchical offline-online framework
Transformer-enhanced metaheuristics
🔎 Similar Papers
No similar papers found.
💼 Related Jobs
