FalconWing: An Ultra-Light Indoor Fixed-Wing UAV Platform for Vision-Based Autonomy
This provides an open-source platform for engineering students and research labs to conduct repeatable indoor UAV experiments, though it is incremental in combining existing methods like Gaussian Splat simulation with lightweight hardware.
The authors tackled the challenge of developing a lightweight indoor fixed-wing UAV for vision-based autonomy, achieving 100% tracking success in leader-follower tasks and 80% success in autonomous landings with zero-shot simulation-to-real transfer.
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.