AI for Autonomous Aerial Systems

Building intelligence
for autonomous
aerial systems.

Researching perception, reasoning, planning and natural-language interaction for UAVs operating in the physical world.

Flood survey · illustrative
Phase
Transit
Alt
62.0 m
Vel
8.4 m/s
Batt
100%
Cov
0%
Det
0
Rescue priority queue0 targets

Survey in progress. No detections yet.

00

The premise

Problem

Most drones still need a pilot. I want to know what it takes to stop needing one.

Someone standing at the edge of a flood does not want a control stick. They want to say what they need and get an answer back. That sentence gets said once, under pressure, by a person with no technical vocabulary, and somehow it has to turn into perception, reasoning, planning, navigation and action on an aircraft with a finite battery and no signal.

Target instruction
"Fly over the flooded region, identify stranded people, map inaccessible roads, and report the highest-priority rescue locations."

Everything on this site works backwards from that sentence. What has to be understood, seen, decided and flown before you can answer it honestly. The console above is that sentence, executed: it surveys, it revises what it believes as the water rises, and it hands back a ranked list.

01

Research pillars

R1

Natural Language UAV Control

Can a spoken instruction become a flight mission that is safe and checkable without a pilot?

R2

Aerial Perception

What does a UAV need to see, at what altitude and in what conditions, before its report can be trusted?

R3

Autonomous Planning & Navigation

How should a UAV decide where to look next when its map is wrong and its battery is finite?

R4

UAVs for Disaster Response

Can a firefighter or police officer use an autonomous aerial system on day one, with no drone training?

R5

Edge AI for UAVs

What is the largest model that can fly, and what does shrinking it cost on the cases you care about?

02

Active project files

03

System architecture · select a layer

04 / 14
04

Open notebook · failures included

EXP-005

Edge object detection benchmark

failed
Hypothesis
Post training INT8 quantisation keeps detection recall within an acceptable margin and still meets the onboard latency budget.
Results
None. The benchmark as I designed it could not produce a number worth trusting.
Failures
I measured on a cold board. Once it reached steady state temperature it throttled, and the numbers got materially worse, so what I had recorded described nothing like a real flight. The INT8 calibration set also came from the same distribution as the test set, which would have inflated quantised accuracy. Two mistakes in one harness.
Next steps
Soak the board to steady state before measuring, use an independent calibration set, and log power next to every latency figure. Single shot timings go in the bin.
This is a static preview of the landing page, architecture explorer and notebook. The full Next.js project in the zip has all nine routes, seven MDX research notes with KaTeX, publication BibTeX generation, sitemap and per-project pages.