Unmanned aerial vehicle (UAV) research requires the integration of cutting-edge technology into existing autopilot frameworks. This process can be arduous, requiring extensive resources, time, and detailed knowledge of the existing system. ROSplane is a lean, open-source fixed-wing autonomy stack built by researchers for researchers. It is designed to accelerate research by providing clearly defined interfaces with an easily modifiable framework. Built around ROS 2, ROSplane allows for rapid integration of low or high-level control, path planning, or estimation algorithms. A focus on lean, easily-understood code and extensive documentation lowers the barrier to entry for researchers. Recent developments to ROSplane improve its capacity to accelerate UAV research, including the transition from ROS 1 to ROS 2, enhanced estimation and control algorithms, increased modularity, and an improved aerodynamic modeling pipeline. This aerodynamic modeling pipeline significantly reduces the effort of transitioning from simulation to real-world testing without requiring costly system identification or computational fluid dynamics tools. ROSplane's architecture reduces the effort required to integrate new research tools and methods, expediting hardware experimentation.

翻译：无人机研究需要将前沿技术集成到现有的自动驾驶框架中。这一过程可能十分艰巨，需要大量资源、时间以及对现有系统的深入了解。ROSplane 是一个由研究人员为研究人员构建的轻量级、开源固定翼自主系统栈。它旨在通过提供定义清晰的接口和易于修改的框架来加速研究进程。ROSplane 围绕 ROS 2 构建，能够快速集成低层或高层控制、路径规划或估计算法。其专注于轻量级、易于理解的代码和详尽的文档，降低了研究人员的入门门槛。ROSplane 的最新进展提升了其加速无人机研究的能力，包括从 ROS 1 到 ROS 2 的过渡、增强的估计与控制算法、更高的模块化程度以及改进的空气动力学建模流程。该空气动力学建模流程显著减少了从仿真过渡到真实世界测试所需的工作量，且无需昂贵的系统辨识或计算流体动力学工具。ROSplane 的架构降低了集成新研究工具和方法所需的工作量，从而加快了硬件实验的进程。