Artificial intelligence on the wing: Fully on-board visual servoing for object tracking with autonomous Nano Unmanned Aerial Vehicles

Nano Unmanned Aerial Vehicle (UAV) platforms are well-suited for tasks in confined spaces, such as indoor single-person tracking. However, they are constrained by payload, and milliwatt (mW) level computing budgets. To address these limitations, we present a fully on-board tracking system featuring Feather You Only Look Once (FeatherYOLO), an ultra-lightweight detector tailored to the embedded processor, and a custom image-based visual servoing controller deployed on a 29 gram Crazyflie 2.1 nano UAV. FeatherYOLO utilizes a depthwise-separable backbone with a decoupled, anchor-free head, requiring 20 thousand parameters, 1.94 million multiply-accumulate operations, and 224 kilobytes of memory. On our self-collected indoor human detection dataset (six participants across five sites) under a cross-subject and cross-environment held-out protocol, the model achieved 99.5% mean Average Precision (mAP) at an Intersection Over Union threshold of 0.50 and 75.0% mAP over thresholds from 0.50 to 0.95. On-board profiling reveals that pure inference consumes 46.5 mW at 150 Frames Per Second (FPS), accounting for less than 1% of the total flight power, and outperforms a recent nano UAV baseline consuming 225.7 mW at 43 FPS. The proposed visual servoing strategy was refined through flight trials and task-driven tuning, transforming detector outputs into stable, bounded velocity commands with hysteresis and filtering for closed-loop indoor tracking. Real-world flight tests validated the tracking performance with an average tracking success rate of 90.0%, succeeding in 36 out of 40 experimental runs. The primary flight challenges identified include collisions and target loss.