AeroDaaS: A Programmable Drones-as-a-Service Platform for Intelligent Aerial Systems

2026-02-28

AI Summary

This work addresses the integration complexity of unmanned aerial vehicles (UAVs) in unified navigation, perception, and intelligent analytics, as well as real-time cross-tier (onboard–edge–cloud) collaboration. To this end, we propose AeroDaaS—the first service-oriented platform that abstracts UAV systems into a composable microservice architecture. The platform encapsulates perception, navigation, and analytics capabilities into low-code (<40 lines), low-overhead microservices compatible with heterogeneous hardware such as the NVIDIA Jetson Orin Nano and RTX 3090. Real-time coordination between trajectory optimization and inference tasks is achieved through dedicated Waypoint and Analytics schedulers. Evaluation across six real-world DaaS applications demonstrates that the system incurs less than 20 ms per-frame processing latency and consumes approximately 1 GB of memory, enabling efficient deployment in both physical and simulated environments.

Technology Category

• Machine Learning: Hardware-aware ML
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Mobility, Driving & Flight

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

The increasing adoption of UAVs equipped with advanced sensors and GPU-accelerated edge computing has enabled real-time AI-driven applications in domains such as precision agriculture, wildfire monitoring, and environmental conservation. However, the integrated design and orchestration of navigation, sensing, and analytics, together with seamless real-time coordination across drone, edge, and cloud resources, remains a significant challenge. To address these challenges, we propose AeroDaaS, a service-oriented framework that abstracts UAV-based sensing complexities and provides a Drone-as-a-Service (DaaS) model for intelligent decision-making. AeroDaaS offers modular service primitives for on-demand UAV sensing, navigation and analytics as composable microservices, ensuring cross-platform compatibility and scalability across heterogeneous UAV and edge-cloud infrastructures. AeroDaaS also supports plug-and-play scheduling modules, including Waypoint and Analytics schedulers, which enable trajectory optimization and real-time coordination of inference workloads. We implement and evaluate AeroDaaS for six real-world DaaS applications, of which two are evaluated in real-world scenarios and four in simulation. AeroDaaS requires less than 40 lines of code for the applications and has minimal platform overhead of less than 20 ms per frame and about 1 GB memory usage on Orin Nano and a AMD RTX 3090 GPU workstation. These results are promising for AeroDaaS as an efficient, flexible and scalable UAV programming framework for autonomous aerial analytics.

Problem

• UAV
• Drone-as-a-Service
• edge computing
• real-time coordination
• intelligent aerial systems

Innovation

• Drone-as-a-Service
• edge-cloud orchestration
• microservice architecture
• real-time aerial analytics
• programmable UAV platform