TURNA VTOL UAV
A 23 kg vertical take-off and landing surveillance UAV with custom-designed motors, encrypted communications, real-time AI target detection, and a TinyML-powered dynamic motor optimization system.
About TURNA
TURNA is a VTOL-type unmanned aerial vehicle designed for surveillance missions. It takes off and lands vertically using motors embedded in the wings, then transitions to horizontal flight for cruise. The telescopic landing gear retracts flush into the body to keep the aerodynamics clean during flight and deploys for landing on any surface.
The airframe is made from PEEK (Polyaryletherketone) and carbon fiber — extremely light but strong enough for the mission profile. With a wingspan of 2.57 meters and a takeoff weight of about 23 kg, it's designed to carry a full sensor payload while maintaining enough endurance for extended surveillance operations.
What makes this project interesting isn't just the airframe — it's the stack underneath. Custom motor designs, encrypted comms, real-time AI running on a Jetson, and a TinyML model that dynamically optimizes motor power mid-flight.
Side and top views of the TURNA airframe design.
Systems & Technology
Every major subsystem — from motor electromagnetics to AI inference — was designed and implemented by our team.
Custom Propulsion System
Three different motor types, all designed from scratch using Ansys RMxprt and validated with Maxwell FEA. The wing-embedded VTOL motors are Interior Permanent Magnet BLDCs producing 15.8 kg thrust each. Outrunner BLDCs near the fuselage handle rapid acceleration, and a central EDF motor delivers high-speed cruise flight.
Computer Vision Pipeline
YOLOv10-B running on the Jetson Orin's NPU — 5.74ms inference latency, 19.1M parameters. Trained on military datasets for target and zone recognition. When fast tracking is needed (like dogfight scenarios), the system automatically switches to KCF tracking at 348 FPS average.
Encrypted Communication
Not just a radio link — the entire comm system uses RSA for client authentication and ECDH (Elliptic Curve Diffie-Hellman) for session key exchange. Even if someone intercepts the data packets, they can't read them. The private key only exists on the UAV at launch.
TinyML Motor Optimization
A 25-neuron RNN model that dynamically optimizes motor power in real-time to conserve momentum. Trained on experimental flight data and achieves 99.27% R² accuracy. Runs directly on the flight controller — no cloud, no latency.
Ground Control Station
The operator connects to the UAV through an encrypted server. Video feed, telemetry, GPS position, and manual override controls — all transmitted over the secure channel. Authentication happens via public/private key exchange before any data flows.
Airframe & Landing Gear
Built with PEEK (Polyaryletherketone) and carbon fiber for a lightweight but incredibly strong airframe. Telescopic spring-loaded landing gear retracts flush into the body during flight to maintain aerodynamic cleanliness, and deploys for any-surface landing.
Encrypted Comms
The communication between TURNA and the ground station isn't just a radio link — it's a full cryptographic pipeline. Before any data flows, the UAV authenticates itself using RSA public/private key exchange. The private key only exists on the physical UAV and is loaded before takeoff.
Once authenticated, ECDH (Elliptic Curve Diffie-Hellman) generates a shared session key without either side revealing their secrets. All telemetry, control commands, and video are encrypted with this key. Even if someone intercepts the packets, they can't read or inject commands.
AI & Computer Vision
Target Detection
YOLOv10-B runs on the Jetson Orin's NPU for real-time object detection. The model is trained on military datasets to recognize targets, zones, and objects of interest. At 5.74ms per frame with 19.1M parameters, it's fast enough for real-time surveillance at flight speed.
For dogfight scenarios where fast tracking beats re-detection, the system dynamically switches to KCF tracking — averaging 348 FPS on test footage.
TinyML Motor Control
Instead of fixed throttle curves, TURNA uses a 25-neuron RNN model running directly on the flight controller to dynamically optimize motor power. The model conserves momentum during transitions and maneuvers — making the UAV more energy-efficient and responsive than rule-based approaches.
Trained on experimental flight data collected by our team, the model achieves 99.27% R² accuracy on motor speed prediction. No cloud required — it runs at the edge with zero latency.
Interested in the technical details?
This project combines aerodynamic design, custom motor engineering, encrypted real-time communications, edge AI, and TinyML — all in a 23 kg airframe. Let's talk if you're working on something similar.
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