Despite the growing interest in low-altitude economy (LAE) applications, including UAV-based logistics and emergency response, fundamental challenges remain in orchestrating such missions over complex, signal-constrained environments. These include the absence of real-time, resilient, and context-aware orchestration of aerial nodes with limited integration of artificial intelligence (AI) specialized for LAE missions. This paper introduces an open radio access network (O-RAN)-enabled LAE framework that leverages seamless coordination between the disaggregated RAN architecture, open interfaces, and RAN intelligent controllers (RICs) to facilitate closed-loop, AI-optimized, and mission-critical LAE operations. We evaluate the feasibility and performance of the proposed architecture via a semantic-aware rApp that acts as a terrain interpreter, offering semantic guidance to a reinforcement learning-enabled xApp, which performs real-time trajectory planning for LAE swarm nodes. We survey the capabilities of UAV testbeds that can be leveraged for LAE research, and present critical research challenges and standardization needs.

翻译：尽管基于无人机的物流和应急响应等低空经济应用日益受到关注，但在复杂、信号受限的环境中协调此类任务仍存在根本性挑战。这些问题包括：缺乏实时、弹性且情境感知的空中节点编排能力，以及专门针对低空经济任务的人工智能集成不足。本文提出了一种基于开放无线接入网络的低空经济框架，该框架利用解耦的RAN架构、开放接口与RAN智能控制器之间的无缝协同，以支持闭环、AI优化且任务关键型的低空经济运营。我们通过一个具备语义感知能力的rApp（作为地形解析器）评估了所提架构的可行性与性能，该rApp为支持强化学习的xApp提供语义引导，而xApp则负责为低空经济集群节点执行实时轨迹规划。本文综述了可用于低空经济研究的无人机测试平台能力，并提出了关键的研究挑战与标准化需求。