ROSplane 2.0: A Fixed-Wing Autopilot for Research

TL;DR

The paper tackles the challenge of integrating research algorithms into UAV autonomy stacks and closing the simulation-to-real gap. It introduces ROSplane 2.0, a lean fixed-wing autopilot built on ROS 2 with a full-state EKF, improved control, and an aerodynamic modeling pipeline, designed for easy customization and modular integration. By moving the stack to a Linux companion computer and leveraging open-source tools like XFLR5/OpenVSP, it enables safe, rapid tuning in simulation before hardware testing, reducing risk and iteration time. Hardware experiments and simulation studies demonstrate comparable performance between simulated and real flights, underscoring the framework's value for reproducible, open-source UAV research with streamlined integration workflows.

Abstract

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. Powered by 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 expensive system identification or computational fluid dynamics tools. ROSplane's architecture reduces the effort required to integrate new research tools and methods, expediting hardware experimentation.

Figures (8)

• Figure 1: Cascaded architecture of ROSplane's modules.
• Figure 2: Aircraft integration pipeline with iterative feedback.
• Figure 3: Comparison of ROSplane and ROSplane 2.0 estimated states on a takeoff and path following sequence in simulation.
• Figure 4: The state machine for different phases of flight.
• Figure 5: Comparison of ROSplane estimated position and RTK GNSS recorded position, alongside commanded path.