OmniDroneX: An LLM-Assisted Holistic Drone-as-a-Service Ecosystem

2026-06-16

AI Summary

This work addresses the rigidity of existing unmanned aerial vehicle (UAV) systems, which lack a unified abstraction bridging high-level task intent and low-level capabilities. The paper proposes OmniDroneX—an extensible, resilient UAV-as-a-Service ecosystem that integrates large language models (LLMs) across multiple system layers for the first time. Through a vendor-agnostic interface (libUAV) and a physics-to-service-oriented abstraction model (PT-SOA), OmniDroneX enables automatic mapping and execution of natural-language tasks into service workflows. The framework innovatively establishes a multidimensional service composition mechanism encompassing physical, spatiotemporal, functional, collaborative, anomaly-aware, and QoS dimensions, thereby supporting autonomous task decomposition, scheduling, and self-evolving execution in complex, dynamic environments.

Abstract

Despite rapid advances in UAV technologies, current deployments remain limited due to several gaps in UAV systems research. To address these challenges, we propose OmniDroneX, a unified Drone-as-a-Service ecosystem, in which drones are transitioned from fixed function platforms into dynamically composable entities that can be integrated with external infrastructures to offer omni-capabilities. OmniDroneX bridges low-level physical primitives with high-level mission intent through a unified vendor-agnostic interface (libUAV) and a formal physical-service abstraction model (PT-SOA). A core innovation is the diverse application of large language models (LLMs) across multiple layers of the OmniDroneX architecture. LLMs are used to assist in identifying and formalizing primitive device functions and abstract service definitions, supporting automated service composition and workflow generation, and enabling interactive, natural-language mission specification and refinement. OmniDroneX also incorporates important categories of composition techniques that are essential in dynamic UAV systems, including physical layer composition for drone capability augmentation, as well as spatiotemporal, functional, collaborative, exception-aware, and QoS-based service compositions. Collectively, these features allow OmniDroneX to serve as a foundation for scalable, resilient, and self-evolving UAV ecosystems operating in complex and dynamic environments.

Problem

• UAV systems
• Drone-as-a-Service
• service composition
• mission intent
• dynamic environments

Innovation

• LLM-assisted UAV
• Drone-as-a-Service
• service composition
• physical-service abstraction
• natural-language mission specification