EventsDrones 2026: Mapping the Knowledge
Published
This submission belongs to the session S6. Drones in Security and Defense of the event Drones 2026: Mapping the Knowledge
Published date
12 Nov, 2026
Contribution&Funding
Conceptualization,
[Barış ÜNVER] ; [Murat KOKLU] ; [Nisanur GENÇ] ; [Mehmet Türkay YOLDAR]
Methodology,
[Barış ÜNVER] ; [Nisanur GENÇ] ; [Mehmet Türkay YOLDAR] ; [Simay ÖZKAN]
Software,
[Barış ÜNVER] ; [Mehmet Türkay YOLDAR] ; [Nisanur GENÇ]
Validation,
[Mehmet Türkay YOLDAR] ; [Barış ÜNVER]
Formal analysis,
[Simay ÖZKAN] ; [Mehmet Türkay YOLDAR] ; [Barış ÜNVER]
Investigation,
[Simay ÖZKAN] ; [Nisanur GENÇ]
Resources,
[Barış ÜNVER]
Data curation,
[Nisanur GENÇ]
Writing - original draft,
[Mehmet Türkay YOLDAR] ; [Nisanur GENÇ] ; [Barış ÜNVER] ; [Simay ÖZKAN]
Writing - review,
[editing] ; [Murat KOKLU]
Visualization,
[Simay ÖZKAN] ; [Nisanur GENÇ]
Supervision,
[Murat KOKLU]
Project administration,
[Barış ÜNVER]
Funding: --
Citation
Barış ÜNVER, Simay ÖZKAN, Mehmet Türkay YOLDAR, Nisanur GENÇ, Murat KOKLU, SmartPilot: A Lightweight, Low-Power Edge AI Module for Intelligent UAV Operations, in Proceedings of Drones 2026: Mapping the Knowledge, Barcelona, 11 November–13 November 2026, MDPI: Basel, Switzerland
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SmartPilot: A Lightweight, Low-Power Edge AI Module for Intelligent UAV Operations

Mehmet Türkay YOLDAR 1
Nisanur GENÇ 1,2
image
1. R&D, TURK AI Artificial Intelligence Systems, Ankara, 06800, Turkey (Türkiye)
2. Department of Computer Engineering, Selcuk University, Konya, 42000, Turkey (Türkiye)
3. R&D, Track AI, Minnesota, 55114, USA
Abstract

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. 

Keywords
autonomous systems
computer vision
deep learning
embedded systems
human-supervised autonomy
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