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.

翻译：无人机正在迅速改变从农业和基础设施监测到物流和国防的众多应用领域。为这些系统引入更高程度的自主性可以同时提升其效能与可靠性。因此，快速设计与部署自主空中系统的能力已具有战略重要性。在2010年代，高性能计算、数据与开源软件的结合催生了当前的深度学习与人工智能热潮，释放了数十年积累的理论研究成果。机器人技术正处在类似变革的边缘。然而，物理人工智能面临独特的障碍，这些障碍常被统称为"仿真到现实的差距"。其范围涵盖建模缺陷，以及垂直集成现场机器人中常见的高度异构硬件与软件系统的复杂性。针对后者，我们推出了空中自主系统栈——一个开源的端到端框架，旨在简化从（GPU加速的）感知到（基于飞行控制器的）行动的完整流程。我们的系统栈支持使用ROS2开发空中自主系统，并为两种最流行的自动驾驶仪——PX4与ArduPilot——提供了统一接口。我们证明该框架支持超过实时速度20倍的端到端完整开发部署栈仿真（包括边缘计算与网络），从而显著压缩基于感知的自主系统的构建-测试-发布周期。