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arXiv · 2505.01383

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

Yan Miao·Will Shen·Hang Cui·Sayan Mitra
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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.

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Yan Miao, Will Shen, Hang Cui, Sayan Mitra. 2025-05-02. FalconWing: An Ultra-Light Indoor Fixed-Wing UAV Platform for Vision-Based Autonomy. https://arxiv.org/abs/2505.01383

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