aerial-autonomy-stack -- a Faster-than-real-time, Autopilot-agnostic, ROS2 Framework to Simulate and Deploy Perception-based Drones

2026-02-06

AI Summary

This work proposes an end-to-end, open-source ROS 2 framework to address key challenges in autonomous drone development, including the sim-to-real deployment gap, complex integration of heterogeneous software and hardware, and prolonged iteration cycles. The framework integrates GPU-accelerated perception modules, a high-fidelity simulation environment, edge computing capabilities, and network communication modeling, while providing unified support for both PX4 and ArduPilot—the two dominant autopilot systems. It achieves, for the first time, full-stack simulation running over 20× faster than real time, significantly accelerating the build–test–deploy cycle for perception-driven drone systems and thereby enhancing both development efficiency and deployment reliability.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Vision for Robotics & Autonomous Driving
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Security and Privacy: Browser and end-point security
• Responsible Web: Consent frameworks and practices on the web

Abstract

Unmanned aerial vehicles are rapidly transforming multiple applications, from agricultural and infrastructure monitoring to logistics and defense. Introducing greater autonomy to these systems can simultaneously make them more effective as well as reliable. Thus, the ability to rapidly engineer and deploy autonomous aerial systems has become of strategic importance. In the 2010s, a combination of high-performance compute, data, and open-source software led to the current deep learning and AI boom, unlocking decades of prior theoretical work. Robotics is on the cusp of a similar transformation. However, physical AI faces unique hurdles, often combined under the umbrella term"simulation-to-reality gap". These span from modeling shortcomings to the complexity of vertically integrating the highly heterogeneous hardware and software systems typically found in field robots. To address the latter, we introduce aerial-autonomy-stack, an open-source, end-to-end framework designed to streamline the pipeline from (GPU-accelerated) perception to (flight controller-based) action. Our stack allows the development of aerial autonomy using ROS2 and provides a common interface for two of the most popular autopilots: PX4 and ArduPilot. We show that it supports over 20x faster-than-real-time, end-to-end simulation of a complete development and deployment stack -- including edge compute and networking -- significantly compressing the build-test-release cycle of perception-based autonomy.

Problem

• simulation-to-reality gap
• autonomous aerial systems
• perception-based drones
• heterogeneous integration
• rapid deployment

Innovation

• faster-than-real-time simulation
• autopilot-agnostic
• ROS2
• perception-based autonomy
• simulation-to-reality gap