Airframe Core
It starts at the core
The central airframe locks together first — the structural heart that will carry every subsystem of the KA-FA1500.
SUAS 2026 — System Assembly
KA-FA1500
Scroll to build the aircraft, piece by piece.
Carbon Structure
Folding carbon arms
Four 20 mm 3K carbon fiber arms mount on RJX folding joints — rigid in flight, compact on the bench.
Propulsion
T-Motor U7 490KV
Four U7 490KV motors and matched propellers spin onto the arm tips to deliver the thrust behind every mission.
Landing Gear
Telescopic stance
The telescopic landing gear reaches down and locks — a stable footprint for touchdown and takeoff.
Avionics & Power
Cube Orange + ArduPilot
A Cube Orange flight controller running ArduPilot slides into the bay, powered by a Leopard 13000 mAh pack.
Sensors & Delivery
Eyes on the mission
The canopy closes over the avionics as the SIYI A8 mini gimbal and the air delivery mechanism come aboard.
SUAS 2026
KA-FA1500
Assembly complete. Scroll on to meet the aircraft.
TECHNICAL DATA
Project Description
About Us
Founded at Atılım University, the Atılım UAV Team is a multidisciplinary engineering group dedicated to pushing the boundaries of autonomous unmanned aerial vehicles. We develop our aircraft almost entirely in-house, managing everything from structural design and aerodynamic optimization to advanced image processing and system integration. Our primary focus for the 2025-2026 academic year is the prestigious SUAS competition, where we aim to showcase our engineering capabilities through complex autonomous missions, precise payload delivery, and real-time target detection. By bringing together aerospace, mechanical, software, computer, and industrial engineers, our team fosters an innovative problem-solving culture that prepares its members for the future of the aviation industry.
Mission Requirements
The SUAS competition challenges teams to execute complex autonomous flight operations. Our Unmanned Aerial System (UAS) is specifically designed to fulfill the following core mission parameters:
- Autonomous Flight: The aircraft must autonomously take off, accurately navigate through a pre-defined series of waypoints within designated flight boundaries, and execute a safe landing without manual intervention.
- Object Detection, Classification, and Localization (ODCL): The system captures aerial imagery of a designated search zone to identify specific targets. It then processes these images to classify target characteristics (shape, color, alphanumeric data) and relays their exact GPS coordinates to the ground station.
- Photogrammetric Mapping: The UAS autonomously captures a series of overlapping aerial photographs of the operational area, which are then processed using photogrammetry to generate a high-resolution 2D map of the field.
- Air Delivery: The UAS performs a precision autonomous airdrop operation. The mechanism is engineered to safely release two distinct payloads—an 8 oz plastic water bottle and a signaling beacon—ensuring they land undamaged at their exact GPS target locations.
Primary Objective
Our primary objective as the Atılım UAV Team extends beyond merely participating in an international competition like SUAS 2026; we aim to produce original and sustainable solutions that contribute to technological advancements in the field of unmanned aerial systems. This project aims to provide a faster, safer, and data-driven operational model by reducing the dependence on human power in disaster response processes. Thanks to its lightweight, portable, and fully autonomous structure, it holds the potential to be a commercially viable solution not only in competition environments but also in the fields of civil defense, search and rescue, and environmental monitoring.
Financial Breakdown
$8,259Total Cost
Electronic Parts 74.2%
Structural Parts 7.7%
Competition 18.2%
Total Project Cost: $8,259
The total budget for the Atılım UAV Team's SUAS 2026 project is optimized to ensure maximum performance and reliability. The majority of the budget (74.2%) was strategically allocated to advanced Electronic Parts to support complex avionics, imaging, and autonomous processing systems. Structural Parts, utilizing cost-effective in-house manufacturing methods, account for 7.7% of the cost. The remaining 18.2% is dedicated to Competition Expenses, ensuring all logistical and operational requirements are met for the event.
- Target Detection: We use a custom Ultralytics YOLOv11 model trained to detect mannequins and tents. The dataset combines public aerial imagery with our own drone footage, shot at competition-representative altitude and in the poses the handbook specifies. Inference runs onboard on an NVIDIA Jetson Orin NX.
- Camera: The camera is a SIYI A8 mini, a 3-axis gimbal-stabilised unit with a 1/1.7" Sony sensor and 81° horizontal field of view. It streams two resolutions at once: 720p to the onboard Jetson for detection, and 1080p to the ground station for mapping and live monitoring
- Mapping: The Risk Mapping requirement is met with a custom OpenCV stitching module built on SIFT and ORB keypoints with homography transforms. It currently merges frames from visual overlap alone; GPS integration for a fully georeferenced orthomosaic is the next development phase.
- Simulation: The full stack is integration-tested before it flies. Gazebo runs the environment, ArduPilot SITL runs the same firmware as the aircraft, and QGroundControl runs the mission. Survey flights execute autonomously and capture geotagged imagery at every waypoint, so mapping can be validated without a flight.
Our aircraft features an innovative foldable architecture designed to maximize durability and portability, strictly adhering to the "Design for Rapid Response" requirements of SUAS 2026.
- Material Selection: The arms and telescopic landing gear are constructed from lightweight, high-strength 20mm 3K Carbon Fiber tubes. The custom motor mounts are CNC-machined in-house from 6061 aluminum and paired with commercial off-the-shelf (COTS) 6061 aluminum folding mechanisms. The main chassis utilizes G10 composite material to ensure maximum signal permeability and optimal RF communication performance.
- Compact & Telescopic Structure: The drone features a fully foldable arm and telescopic landing gear design. This tool-less mechanism allows for rapid deployment, getting the system flight-ready in minutes.
- Volume & Dimensions: In its flight configuration (arms extended), the aircraft measures 21.2 x 21.2 inches (54x54 cm) with a height of 11 inches (28 cm). When folded into its transport mode, the footprint is drastically reduced to just 13 x 13 inches (33x33 cm). This compact volume easily complies with the "Carry-On" luggage standards specified in the rulebook, providing a significant advantage in logistical and rapid deployment operations.
To minimize wiring complexity and weight, our system operates without a traditional PDB. We utilize the Orange Cube Plus's native power module for accurate voltage/current telemetry. High-bandwidth data routing is handled by a SwitchBlox Ethernet switch, which splits the SIYI A8 Mini camera stream simultaneously to the onboard Jetson Orin for real-time inference and to the Ground Station for live monitoring. System safety is strictly ensured by a 500A-rated physical kill switch. Thrust is generated by high-efficiency T-Motor U7 Lite V2.0 (490 KV) motors, driven by an RTF 80A 4-in-1 8S Lite AT ESC to centralize power delivery. Our power plant consists of Leopard Power 13000 mAh 25C 2S batteries configured in a 6S array (3 in series, 2 parallel packs), providing optimal discharge rates.
Air Delivery Mechanism
To strictly comply with the competition's requirement against freefall deliveries without retardants, the autonomous payload drop is executed via a custom mechanical pulley system. The controlled descent is driven by an 800 RPM 6V DC motor, precisely managed by an MC33886 H-Bridge driver (powered by a dedicated BEC). An SG90 micro servo is seamlessly integrated to securely lock the payload in place during flight maneuvers and release it instantly upon reaching the target coordinates.
Navigation & Controls
- Autonomous Flight System: Our core autonomous operations are powered by the Orange Cube Plus flight controller running ArduPilot firmware, paired with a CUAV Neo 3 Pro GNSS module for high-precision positioning.
- Mission Capabilities: The system is engineered to execute complex autonomous missions end-to-end. This includes precise waypoint navigation, scanning the search boundary for photogrammetric mapping, executing targeted payload drops on the detected mannequin and tent, and performing a safe Return to Home (RTH).
- Companion Computer & Unified Comms: A Waveshare Jetson Orin NX 16GB serves as the onboard companion computer handling heavy processing tasks. To ensure maximum efficiency and reliability, all critical communications—including HD video transmission, RC control, and Ground Control Station (GCS) telemetry—are unified and transmitted seamlessly through a single SIYI HM30 datalink system.
Sponsors
Gezerler İnşaatÇatkaya GayrimenkulÖzcansu Tanker Su TaşımacılığıWupsoftFSM DemirbaşPegem AkademiAselsanDemirbaş UnBallıkuyumcu Offroad
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Download the original one-pager (PNG)SYSTEM OVERVIEW
01
SUB-01 / SOFTWARE / VISION & GUIDANCE
VISION & AUTONOMOUS GUIDANCE
Burak Selek · İpek Arslan — image processing / Serdar Anıl Demirbaş — integration
Detection runs onboard. A YOLO11m model executes on an NVIDIA Jetson Orin NX 16GB, reading a 1080p RTSP stream from a SIYI A8 mini gimbal camera with an 81° horizontal field of view. The flight configuration is full-frame inference at 640 px, which holds 15–20 FPS on the aircraft — fast enough for the autopilot to act on a detection while the target is still in frame.
The first model failed in the field: detections were lost at altitude. The root cause was a dataset without small-object examples and scale augmentation left disabled. Rebuilding the dataset — 16,015 images merged, deduplicated and class-balanced — and retraining at higher resolution brought mAP50 to 0.950 on a validation set built only from real aerial footage, not web imagery.
Confirmed detections are handed to the autopilot as a position, so the aircraft positions itself over the target autonomously instead of waiting for an operator.
READOUT
MODEL
YOLO11m
ONBOARD COMPUTE
NVIDIA Jetson Orin NX 16GB
SENSOR
SIYI A8 mini · 81° HFOV · 1080p RTSP
FLIGHT CONFIG
640 px full-frame · 15–20 FPS
mAP50
0.950 — aerial-only validation set
DATASET
16,015 images · deduplicated, rebalanced
02
SUB-02 / SOFTWARE / MAPPING
MAPPING — FASTMOSAIC
Ünal Namdar · Simay Akgündüz — mapping specialists
Off-the-shelf photogrammetry was too slow and too opaque for a competition timeline, so the team wrote its own. FastMosaic selects frames using GPS telemetry, then aligns them with ORB feature matching and RANSAC outlier rejection, writing the mosaic straight out as a PNG in submission format — no intermediate point cloud, no third-party desktop stage.
It also runs during the flight. A live coverage preview shows the operator which part of the search area has already been mapped, so gaps are caught while the aircraft is still airborne instead of after landing.
READOUT
PIPELINE
In-house — FastMosaic
FRAME SELECTION
GPS-assisted from telemetry
ALIGNMENT
ORB features + RANSAC
OUTPUT
PNG, submission format, no post-stage
IN-FLIGHT
Live coverage preview for the operator
03
SUB-03 / SOFTWARE / SIMULATION & INTEGRATION
SIMULATION & SYSTEM INTEGRATION
Serdar Anıl Demirbaş — software team leader, system integration
Nothing new flies before it has flown in simulation. The environment combines Gazebo, ArduPilot SITL, ROS 2 and pymavlink — but the part that matters is the architecture. It is distributed: the real Jetson joins the network as its own node and camera imagery arrives over RTSP exactly as it does in flight. The flight software is therefore exercised on flight hardware without the aircraft leaving the bench.
Failure cases are rehearsed here first. GPS degradation and EKF failure, telemetry link loss and low-battery thresholds were all triggered in SITL, and the autopilot response — loiter, altitude hold, continue-then-return, geofence return — was verified before any of it was trusted in the air.
READOUT
STACK
Gazebo + ArduPilot SITL + ROS 2 + pymavlink
ARCHITECTURE
Distributed — real Jetson in the loop
VIDEO PATH
RTSP, identical to the flight path
FAILSAFES REHEARSED
GPS/EKF loss · link loss · low battery · geofence
AUTOPILOT / GCS
ArduPilot · Mission Planner
04
SUB-04 / MECHANICAL TEAM
AIRFRAME & STRUCTURES
Suavi Yiğit Ölmez — lead / Muhammed Arslan · Mehmet Afşın Demirkasımoğlu · Yiğit Karkın · Atakan Akcan · Yaren Civan
The configuration was not chosen by preference. Candidate layouts were scored with the Analytic Hierarchy Process on the Saaty 1–9 scale, turning engineering judgement into a pairwise comparison matrix. The quadrotor won on the combined criteria, and the decision is reproducible rather than asserted.
The frame is deliberately hybrid. Carbon fibre booms carry the load, while the central plates are 2 mm G10 composite. G10 is denser than carbon fibre, and that is the trade: it is RF-transparent, so the avionics deck does not become a Faraday cage around the antennas. Finite element analysis was run on the arm-to-body joints and motor mounts for the 20 kg peak thrust case.
The structure is designed to a 3.33 g limit load factor — aggressive next to the 1.5–2.0 g envelope typical of commercial heavy-lift platforms — which is what buys wind rejection and rapid acceleration rather than headline speed.
READOUT
CONFIGURATION
Quadrotor — selected by AHP, Saaty 1–9
MATERIALS
Carbon fibre booms + 2 mm G10 plates
WHY G10
RF-transparent — protects the comms link
LIMIT LOAD FACTOR
3.33 g
Vne
20 m/s — sized for 15 kt gust loading
ANALYSIS
FEA on arm joints and motor mounts @ 20 kg
OPERATING WEIGHT / MTOW
6.7 kg / 11 kg
05
SUB-05 / ELECTRONICS & COMMUNICATIONS
AVIONICS & COMMUNICATIONS
Zeynep Sena Polat — lead / Utku Oğul Bolat — ground control station operator
The power and propulsion chain was sized before anything was bought. A power budget built from estimated draw set the battery, the DC-DC converters and the connectors. The result is roughly 22.5 kg of peak static thrust against a 6.7 kg mission weight — a 3.36:1 thrust-to-weight ratio, falling to 2.05:1 at the 11 kg structural MTOW. That margin exists to reject wind, not to win races.
It was validated on a calibrated static thrust stand before it flew: throttle stepped to 100 % while motor temperature, battery voltage sag and ESC telemetry were logged. Telemetry and MAVLink were verified through Mission Planner, and the combined video and control link was bench-tested for zero packet drop at full throughput.
The onboard computer is a live constraint, not a free addition. Its 25 W peak draw pushed battery capacity up and pushed directly against the weight target — a trade the team took deliberately in order to keep detection onboard.
READOUT
FLIGHT CONTROLLER
Pixhawk-class · ArduPilot firmware
GROUND STATION
Mission Planner
PEAK STATIC THRUST
≈ 22.5 kg
THRUST-TO-WEIGHT
3.36:1 @ 6.7 kg · 2.05:1 @ 11 kg MTOW
ENDURANCE
≈ 24 min @ 6.7 kg
COMPUTE POWER BUDGET
25 W peak — Jetson Orin NX
BENCH VALIDATION
Static thrust stand to 100 % throttle
06
SUB-06 / MECHANICAL + AVIONICS — JOINT
PAYLOAD & RELEASE
Mechanical team and electronics & communications team, jointly
The release system is a motorised winch with a servo-actuated lock on a reinforced sub-frame. The lock holds with zero power draw — and that was tested rather than assumed: a payload matching the competition suite in mass was hung from it and left, then checked for slippage over time.
Actuation is commanded through the Jetson control loop, and the bench test measured the delay between the deployment signal and the physical release. Drop reliability was exercised from a range of altitudes before the mechanism was ever flown loaded.
READOUT
MECHANISM
Motorised winch + servo-actuated lock
HOLDING
Zero-power lock, slip-tested under static load
COMMAND PATH
Jetson → autopilot → release
BENCH TEST
Signal-to-release latency measured
FLIGHT VALIDATION
Multi-altitude drop trials before loaded flight
TEAM TO FILL
Measured release latency (ms) and drop accuracy
FIELD EVIDENCE / END-TO-END TEST
ONE COMPLETE RUN, START TO FINISH
In the one comprehensive end-to-end field test the team has flown, the aircraft found both targets — the mannequin and the tent — from roughly 20 m above ground, handed their positions to guidance, flew to each of them autonomously and released the payloads. The water bottle landed about 5 m from the mannequin.
Detection, localisation, guidance and release ran as one chain, with no operator taking over in the middle. That is the whole point of the six subsystems above: individually they are components, and together they either close the loop or they do not. On that run, they closed it.
n = 1
This is one comprehensive test, not a statistic. It is the strongest evidence the team currently holds, and it is reported as exactly that: a single successful end-to-end run. Repeat trials are the next item on the schedule, and this page will be updated with the sample size once they are flown.
0.950
mAP50 · AERIAL-ONLY VALIDATION SET
16,015
IMAGES · CONSOLIDATED DATASET
15–20
FPS ONBOARD · 640 PX FULL-FRAME
~20 m
AGL · BOTH TARGETS DETECTED
METHOD / VERIFICATION LADDER
HOW WE KNOW IT WORKS
SIX HABITS, APPLIED TO EVERY SUBSYSTEM
SIMULATE FIRST
Autonomous mission logic, waypoint execution and failsafe triggers are exercised in Gazebo and ArduPilot SITL before any hardware is at risk. Crosswind, bad weather and sensor failure are cheap in software and expensive in the field.
BENCH BEFORE FLIGHT
Propulsion is run on a calibrated static thrust stand to full throttle with motor temperature, voltage sag and ESC telemetry logged. Sensors are calibrated against EMI, the data link is tested for zero packet drop, and the release lock is load-tested for slippage.
PROGRESSIVE FLIGHT LADDER
Four sequential stages: unloaded manual, unloaded autonomous, loaded manual, loaded autonomous. Aerodynamic stability is confirmed before autonomy is introduced, and autonomy before payload dynamics.
SAFETY PROTOCOL
Props-off policy for indoor bench testing, fireproof LiPo bags, a five-metre safety perimeter before arming, and immediate manual takeover below five metres altitude.
DECIDE WITH NUMBERS
The configuration was chosen with the Analytic Hierarchy Process rather than by preference. The power budget was computed from estimated draw before procurement. Structural joints were checked with FEA at peak thrust.
REVISE FROM TEST DATA
The vision model was rebuilt because it failed in the field, not because it looked wrong on paper. Every flight is followed by inspection for loosening, cracks, vibration and alignment, and the findings go back into CAD.