Autonomous Drones and Their Future Importance

Consider this your two-minute hangar brief. Between February 2019 and November 2020, I served as an Expert Software Engineer at STM Defence Technologies in Ankara. My team and I wrote the operational brains for Kargu, a loitering munition that can patiently orbit before striking, and Togan, a reconnaissance UAV that refuses to land until it has found every point of interest. Coffee was strong, timelines were tighter than rotor tolerances, and every build had to keep aircrews safe.

What “Mission Control” Actually Means

Mission-control is where all the distributed systems magic happens. We carved the stack into micro-services long before it was fashionable in defense:

• Real-time messaging layer in modern C++ with Boost ASIO to keep telemetry, video, and mission commands synchronized over noisy tactical radios.
• Qt/QML operator console with latency budgets stricter than airport security, so a pilot could re-task three airframes before finishing a sentence.
• Built-in-test harnesses that power-on self-check the avionics, gimbals, and payloads; if a motor twitched the wrong way, we knew before the prop spun up.

I spent as much time in the hangar as at the keyboard. Working shoulder-to-shoulder with avionics, payload, and flight-test teams meant translating requests like “make it hover smarter when the wind gusts” into resilient control loops and telemetry dashboards.

Lessons from the Hangar Floor

1. Swarms demand empathy. The pilots named their drones. When a swarm lost a member, morale dipped. We built UI affordances that acknowledged each airframe as a teammate, not a widget.
2. Latency is the silent saboteur. A 200 ms lag at a desk is nothing; in contested RF environments, it’s the difference between a graceful reposition and a lawn dart. We optimized serialization like our coffee budget depended on it.
3. Testing never sleeps. Continuous built-in tests ran between sorties, catching sensor drift or loose connectors. Trust is earned every take-off.

Where the Technology Is Headed (and Why I’m Excited)

• Onboard model compression lets edge AI classify targets, detect anomalies, and perform collaborative SLAM without hauling gigabits back to base.
• Mesh networking + directional antennas give swarms self-healing comms; lose one repeater and the rest re-route faster than your home Wi-Fi.
• Digital twins simulate flight plans against weather, terrain, and jamming profiles before we commit real rotors—think of it as rehearsal dinner for drones.
• Human teaming tooling is finally catching up. Adaptive autonomy levels and explainable cues help operators stay in the loop without feeling like they’re babysitting a sky full of toddlers.

Beyond Defense: The Spillover Effect

Everything we hardened for military resilience is marching straight into civilian life:

• Disaster response – Autonomous swarms can map collapsed buildings, deliver medical kits, and maintain comms when cell towers fail.
• Infrastructure inspection – Pipelines, bridges, and offshore rigs will rely on self-diagnosing fleets that log structural anomalies and order replacement parts before humans notice the rust.
• Environmental stewardship – Persistent UAV networks can monitor forest health, track illegal fishing, and even drop seed pods post-wildfire.
• Logistics – Think short-haul cargo drones coordinating like bee colonies, optimizing routes while obeying airspace rules nobody wants to recite at parties.

The Human Factor (a.k.a. Why Humor Matters)

When you’re debugging a swarm in a wind tunnel at midnight, a joke about “teaching drones to respect personal space” goes a long way. The tech matters, but the culture matters just as much. Empowered operators, collaborative engineers, and transparent mission data are the secret ingredients for safe autonomy.

What Keeps Me Busy Now

These days I’m a Senior Software Engineer at Avion Full Flight Simulators in the Netherlands, building the C/C++ and Scala pipelines that push 50 GB/s of simulator telemetry through gRPC and bounded backpressure without spilling coffee on the instructor console. I pair those services with Svelte dashboards so crews can diagnose actuators before the motion base squeaks. On the academic side I recently finished my PhD in Secure & Distributed Machine Learning at Embry-Riddle, where I fortified proof-of-learning protocols with model watermarking so spoofers have a harder time stealing credit. The mission hasn’t changed: ship systems pilots trust—preferably before the Redis cluster asks for a type rating.

References

[1] STM Kargu – https://www.stm.com.tr/en/products/kargu
[2] STM Togan – https://www.stm.com.tr/en/products/togan