Small UAVs are increasingly relevant to security and defense missions, yet onboard autonomy remains constrained by mass, power, latency, and integration complexity. We present a 22 g edge-AI module, with an accelerator, perception network, and control stack co-designed for a 6 W peak budget, that adds human-supervised perception, tracking, and target-directed control to fixed- and rotary-wing UAVs without replacing the existing flight controller.
A purpose-designed joint detection-and-embedding (JDE) network detects and associates targets in a single inference pass. A reinforcement-learning policy developed and trained in simulation converts its outputs into thrust and roll-, pitch-, and yaw-rate commands. The module therefore occupies the pilot's role rather than the flight controller's, issuing the same high-level commands a human pilot would. The operator retains supervisory authority and immediate override.
The INT8-quantised perception network occupies 0.73 MB and executes in 12 ms at 320 × 320, with 15.2 ms of p99 latency; quantisation reduces mAP@0.5 by only 0.009. Detection achieves 0.746 mAP@0.5 in-domain and 0.614 on the AOT benchmark. Input mapping dominates quantisation error; whole-frame letterboxing reduces AOT mAP@0.5 to 0.127, whereas native-scale cropping restores 0.614.
Closed-loop evaluation covered 350 engagements in a DCS-based simulator, spanning 14 target types and 25 approach geometries, with the physical module commanding an unmodified commercial flight controller. Averaged over flights, field-of-view retention over measurable frames and correct-identity tracking over visible frames both reached 94.7%. The closing rate exceeded 1 m/s for 77.2% of frames, and 47.7% of engagements ended in a successful intercept. Flight trials on physical airframes await authorisation from national authorities.
Hardware–network co-design brings a complete perception-to-control layer within the mass and power envelope of small UAVs, without displacing the existing flight controller.