PROJECT 02 / AEROSPACE & AI RESEARCH LOG · 2026
AN EXPERIMENT IN ACCESSIBLE INTELLIGENCE

Make flight
intelligence
accessible.

Exploring how reused smartphone computing can help bring perception and AI capabilities to more affordable intelligent aircraft.

VISION / PERCEPTION
MOBILE COMPUTE
Abstract fixed-wing aircraft engineering illustration, conceptual rather than a real aircraft photograph AIRFRAMECONTROLCOMPUTE
CONCEPTUAL AIRFRAME VISUALIZATION ≠ FLIGHT TEST
001 / SYSTEM STUDY N 35.0° / CONCEPT
01 / WHY IT MATTERS

“The intelligence inside an aircraft shouldn't be determined only by what someone can afford.”

A second life
for everyday compute.

Smartphones already contain processors, cameras, and sensing hardware. This project explores how that computing capability could be repurposed alongside dedicated flight-control hardware — rather than replacing critical real-time control with an experimental device.

A / 01

Reused mobile compute

Explore existing phone processors, cameras, and sensors for high-level perception tasks.

B / 02

Dedicated flight control

Keep safety-critical stabilization on suitable flight-control hardware, with defined interfaces.

C / 03

Fixed-wing platform

Evaluate the integration through controlled tests before claiming practical autonomous flight.

ⓘ

Design intent, not a completed integration claim. The diagram explains the proposed research direction. The recorded tests below are separate evidence and do not establish onboard phone-to-flight-controller integration.

Real test records.
Inspectable, not embellished.

Explore a recorded ArduPlane software-in-the-loop mission, the original 72-case environment campaign, and an independent Android camera-and-IMU capture test. This viewer is read-only and does not connect to or command an aircraft.

FLIGHT RESEARCH VIEWER / READ ONLYOpen full research viewer ↗
The flight path and telemetry are from a recorded ArduPlane SITL simulation, not a physical flight. The 72-case campaign includes 29 normal passes, 35 normal failures and 8 guard-pass-only cases. The Android camera/IMU capture was an independent device test; it was not integrated with the flight controller.

Separate what is tested
from what is still a goal.

This project combines several kinds of work. The distinctions matter: a simulation, a device-level experiment, and an integrated aircraft test are not interchangeable.

01 / SOFTWARE-IN-THE-LOOP

ArduPlane mission playback

A recorded SITL mission and telemetry can be inspected in the research viewer.

RECORDED SIMULATION This is not a physical flight.
02 / ENVIRONMENT CAMPAIGN

72-case test matrix

The recorded campaign contains 29 normal passes, 35 normal failures, and 8 guard-pass-only cases.

RECORDED SOFTWARE TESTS These results describe the test campaign, not autonomous flight performance.
03 / DEVICE EXPERIMENT

Android camera & IMU capture

An independent Android device test explored camera and motion-sensor capture.

DEVICE-LEVEL TEST It was not integrated with the flight controller.
04 / RESEARCH DIRECTION

Phone-assisted aircraft intelligence

The wider architecture remains an exploration: use accessible mobile compute for higher-level perception while retaining suitable dedicated hardware for safety-critical control.

NOT YET VALIDATED AS AN INTEGRATED SYSTEM Future work must verify interfaces, latency, reliability, and safe operating limits.

A PRACTICAL AMBITION

From available parts
to real possibility.

I want to turn this exploration into affordable, reliable intelligent aircraft for practical tasks — without treating a concept, a simulation, and a real flight as the same achievement.

Back to selected work ↗
01

Verify the architecture

Document actual interfaces, hardware limits, and test outcomes.

↗
02

Improve integration

Address reliability, latency, safety constraints, and useful perception tasks.

↗
03

Build for accessibility

Keep costs and real-world usability central to future development.

↗