arXiv:2606.24979v1 Announce Type: new Abstract: Unmanned aerial vehicles (UAVs) are increasingly employed in urban inspection tasks, where reliable communication is critical but challenging due to the severe spatial channel heterogeneity. To address the issue, in this paper, we focus on the communication-aware path planning for multi-UAV tasks, and propose a channel knowledge map (CKM)-driven trajectory planning framework which integrates the channel modeling and trajectory decision-making. Specifically, we apply the diffusion model to construct a time-accumulated CKM and achieve the accurate
The increasing deployment of UAVs in complex urban environments necessitates sophisticated communication-aware path planning to ensure reliable operations, driven by advancements in AI and sensing technologies.
This development represents a critical step towards more autonomous and reliable UAV operations for essential tasks like urban inspection, potentially reducing human intervention and improving efficiency and safety.
UAVs can now leverage real-time channel knowledge maps to dynamically optimize their trajectories for robust communication, moving beyond pre-programmed paths or reactive adjustments.
- · UAV manufacturers
- · Urban planning and infrastructure sectors
- · AI/ML model developers
- · Smart city solution providers
- · Manual inspection services
- · UAV operators relying solely on traditional GPS/pre-programmed routes
More efficient and reliable urban inspection and data collection using multi-UAV systems becomes feasible.
The integration of AI-driven communication management could accelerate the adoption of UAVs for other critical infrastructure monitoring and delivery services.
Enhanced UAV autonomy and reliability could reduce regulatory hurdles for broader urban airspace integration, potentially leading to new economic models and services.
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