Agentic GenAI-Enabled Resource Allocation for Low-Altitude Embodied Intelligence

Abstract

Low-altitude embodied intelligence (LAEI) has emerged as a promising solution for operational efficiency and sustainability of the emerging low-altitude economy via perception–reasoning–action loops. The ground base stations with limited service coverages fail to achieve ubiquitous connectivity in widespread environments. The aerial agents embedded in flying bodies ensure pervasive intelligence across dynamic three-dimensional spaces. However, the joint optimization of flight trajectories and resource allocation for hierarchical UAV networks introduces large state and action spaces, posing significant challenges for real-time mission execution. In this paper, we propose an agentic Generative Artificial Intelligence (GenAI)-based LAEI framework. In the framework, a joint optimization problem is formulated to minimize long-term average energy consumption while ensuring task queue stability and satisfying spatial kinematic constraints. The Lyapunov optimization technique decomposes the long-term energy minimization problem into deterministic per-slot sub-problems with low computational complexity. A diffusion-based GenAI algorithm synthesizes optimal trajectories through an iterative denoising process, where the model-based resource allocation problem serves as guidance to accelerate convergence. Finally, extensive simulation experiments indicate that the proposed GenAI-enabled algorithm outperforms other baseline schemes, delivering minimized energy consumption and enhanced resource utilization in dynamic low-altitude embodied intelligence environments.