FalconWing: An Ultra-Light Indoor Fixed-Wing UAV Platform for Vision-Based Autonomy

TL;DR

FalconWing is a lightweight indoor fixed-wing UAV platform designed for vision-based autonomy, validated through successful simulation-trained controllers that transfer to real hardware in challenging tasks.

Contribution

This work introduces FalconWing, an ultra-light indoor fixed-wing UAV with a novel hardware and software stack, including a photorealistic simulator for developing vision-based controllers.

Findings

100% tracking success in leader-follower tasks

80% success rate in autonomous landing

Zero-shot transfer of controllers from simulation to real hardware

Abstract

We introduce FalconWing, an ultra-light (150 g) indoor fixed-wing UAV platform for vision-based autonomy. Controlled indoor environment enables year-round repeatable UAV experiment but imposes strict weight and maneuverability limits on the UAV, motivating our ultra-light FalconWing design. FalconWing couples a lightweight hardware stack (137g airframe with a 9g camera) and offboard computation with a software stack featuring a photorealistic 3D Gaussian Splat (GSplat) simulator for developing and evaluating vision-based controllers. We validate FalconWing on two challenging vision-based aerial case studies. In the leader-follower case study, our best vision-based controller, trained via imitation learning on GSplat-rendered data augmented with domain randomization, achieves 100% tracking success across 3 types of leader maneuvers over 30 trials and shows robustness to leader's appearance shifts in simulation. In the autonomous landing case study, our vision-based controller trained purely in simulation transfers zero-shot to real hardware, achieving an 80% success rate over ten landing trials. We will release hardware designs, GSplat scenes, and dynamics models upon publication to make FalconWing an open-source flight kit for engineering students and research labs.