空域自主栈 -- 一个优于实时的、与自动驾驶无关的ROS2框架，用于模拟和部署基于感知的无人机
aerial-autonomy-stack -- a Faster-than-real-time, Autopilot-agnostic, ROS2 Framework to Simulate and Deploy Perception-based Drones
AI总结：
本文提出空域自主栈，一个基于ROS2的开源框架，支持快速模拟和部署基于感知的无人机系统，通过统一接口支持PX4和ArduPilot，实现20倍于实时的仿真速度，缩短开发测试周期。
摘要：
无人飞行器正在迅速改变多个应用，从农业和基础设施监测到物流和国防。增加这些系统的自主性可以同时使它们更有效和可靠。因此，快速工程和部署自主飞行系统的能力变得战略重要。在2010年代，高性能计算、数据和开源软件的结合导致了当前深度学习和AI的繁荣，解锁了数十年的理论工作。机器人正处于类似的转变边缘。然而，物理AI面临独特的挑战，通常统称为“仿真到现实差距”。这些挑战从建模不足到垂直整合通常在野外机器人中发现的高异质硬件和软件系统的复杂性。为了解决后者，我们引入了aerial-autonomy-stack，一个开源、端到端的框架，旨在从（GPU加速的）感知到（飞行控制器基于的）行动的管道进行流线化。我们的栈允许使用ROS2开发空中自主性，并为两种最流行的自动驾驶仪提供共同接口：PX4和ArduPilot。我们展示了它支持超过20倍于实时的完整开发和部署堆栈的端到端模拟，包括边缘计算和网络，显著压缩了基于感知的自主性的开发测试发布周期。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
