At this very moment,
there are approximately 15,000 aircrafts flying around the globe.
From airplanes to satellites,
they are responsible for transporting everything
from information and communication to cargo and people.
Air travel, once thought to be a luxury,
is now one of the most efficient and effective
modes of transportation in the modern world.
Yet two companies,
Inmarsat, the world leader in global satellite communications,
and A-techSYN, experts in unmanned aerial systems,
believe we've only just begun to scratch the surface
of what is truly possible when it comes to aviation.
Today, UAVs are very restricted to the line of sight.
They can't fly too far.
They need a lot of airspace management oversight.
I'm Tony Spouncer, I work for Inmarsat.
I am Senior Director in the Unmanned Aerial Vehicles
and Unified Traffic Management group that we've newly established.
I think if you were to say that there are 60,000 commercial air transport aircraft flying today,
air traffic control cover those,
well, all the indications are that there'll be 10 million commercial UAVs flying by 2030.
Somewhere in the region 600,000 globally,
will require beyond visual line of sight.
That's a tenfold increase in air traffic.
The problem that we're trying to solve is,
in the drone industry, there are a lot of components
that need to act together as a system.
My name is Gokhan, I'm the Founding Partner and CEO of A-techSYN.
There are multiple problems actually, that are involved in the project.
One of them is remotely controlling UAVs doing that,
regardless of where the UAV is, and where the pilot is.
So making it possible that any pilot in the world
can control any other UAV anywhere in the world.
I'm Osman, I'm one of the founding partners in the company.
The process of going from manned planes
to a sky filled with millions of unmanned, fully autonomous aircraft is complex,
and has never been attempted at the scope and scale
in which Inmarsat and A-techSYN believe is possible.
The concept of having sufficient automation
where you can go from two pilots on board
to single pilot operations to remote pilot operations
in a controlled staged manner,
and ultimately have a room with a few pilots
controlling, you know, a fleet of vehicles,
aircraft, UAVs flying down corridors,
is well within our grasp.
But there is no way right now, no platform, no nothing
that enables that cooperation and collaboration between the components.
To start building the infrastructure,
the teams first need to prioritize the systems.
Looking at each one through the lens of safety,
adaptability, and feasibility.
We prioritize safe flight.
So we went in with the command and control first,
and we tried to build a system
that can enable operators to operate drones from anywhere in the world.
We needed to create an adapter that translates the communication
between the UAV autopilot and the cloud.
So we already have an existing autopilot solution.
We already have a protocol
that the autopilot knows how to talk, how to speak,
but it's not suitable for an IoT communication.
So we had to have that adapter in between.
AWS joined the team, playing a vital role
in helping to successfully bridge the gap between the systems and the technologies,
creating an integrated prototype to work from and expand upon.
The problem we're solving in this prototype is how to connect a UAV
to a collection of networks, both RF LTE, SATCOM,
and provide low latency telemetry and video data
to operators both close by and around the world.
I'm David Goehrig, I'm a lead prototype architect for AWS.
One of the difficulties we have
is, this vehicle needs to operate safely in a wide range of operating theatres.
And, as such, we need to be able to communicate
command and control messages with low latency to the vehicle.
Within Inmarsat, we have an L-band safety grade network
that we use for maritime safety of life at sea,
and we use for air traffic control for communication, navigation and surveillance.
Whether UAV works is it connects via RF, SATCOM and LTE connections.
The RF is used primarily for command and control from a local pilot.
For the SATCOM and the LTE,
we use that to send telemetry and video data to the cloud.
On the cloud side, we collect the data
from an onboard single board computer called AWS Elemental Link.
It compresses the data and sends it with the necessary adjusting for the bandwidth
as necessary as we switch communication mechanisms.
So for example, with the LTE connection, we have high bandwidth and low latency.
But for the SATCOM connection, we have low bandwidth and high latency.
For the telemetry data,
we send it over whatever channel happens to be most available.
So we may use the LTE or the SATCOM or even go over the RF link.
The telemetry data is then stored in AWS, such as in Amazon S3,
as part of our data lake,
and then fed into the ML pipeline via AWS lambda.
From the standpoint of the cloud side,
we also have a serverless web native application
that is served out of Amazon S3, hosted on Amazon CloudFront,
and uses Amazon API gateway to provide interfaces to AWS Lambda functions,
which perform the backend processing.
This also allows us to send via AWS IoT Core command and control messages
back over the telemetry link to the UAV, to inform it to perform different operations,
such as updating its flight path or changing the camera feeds.
Creating synergy between the complex components was just the beginning.
AWS also utilized their machine learning expertise
to push the capabilities of the autonomous aircraft prototype,
and their systems to the next level.
AWS is using ML technology
to improve the safety and operations of the UAV flights
through automated anomaly detection
and continuous monitoring of flight performance.
I'm Paul Barna, I'm a data scientist technical lead within AWS,
part of the EMEA prototyping team.
And for this project, I'm in charge of leading the machine learning
and data science activities,
mainly covering the scientific modeling exercise,
as well as supporting the build phase of the end to end machine learning workflow.
The Machine Learning workflow has three distinctive components.
The first one is the feature engineering stage.
Here is using Amazon SageMaker Processing as an infrastructure.
And because we only have access to a limited amount of historical telemetry data,
we had to engineer a hybrid formulation.
I'm saying hybrid because most of the usual suspects
within the space of anomaly detection systems,
they're either using physics based modeling or a data driven modeling.
But we combined the two approaches.
So we used on board physics based estimators,
which are basically some mathematical representations,
some analytic equations of the aircraft dynamics.
And we used them to recover most of the outputs
and some of the interim states in order to craft our own feature space.
So we ended up with the altitude and the heading targets,
which are basically the pitch, roll, yaw angles and their corresponding angular speeds.
And we also integrated the real time observations
coming from the accelerometer and gyroscope data.
And as an output of this feature engineering step,
we ended up with a set of indicators,
which are basically representing,
the distance the divergence between the projection and the actuals.
For the second part, we are dealing with the model build stage.
And here we're using Amazon SageMaker Training
to be able to train a model which learns a low dimensional representation
of the aircraft nominal behavior.
And out of all the telemetry data that we had,
we only selected the ones where the aircraft
is functioning within its operating window.
So we use the auto encoders, which are unsupervised learning methods.
They're using neural networks.
And they use back propagation to learn an approximation
of your underlying process, the identity functions,
whereas the reconstructed outputs of this model are as similar to the inputs.
Third stage is the model deployment part.
And here we're using Amazon SageMaker Neo
to compile the model for NVIDIA architecture.
We're also using SageMaker Edge Manager
to pack all the model artifacts and deploy them to the edge.
And for the inference stage, we are building a custom edge application
which runs a permanent loop.
And with this loop we're computing on the fly,
multi dimension, multi temporal tensors.
And we also use the reconstruction loss metric
to compute the errors,
the residuals between the reconstructed outputs and inputs.
Once we have this, then we convert everything into an overall anomaly score.
And this is used down the line by the diagnostic model
to continuously monitor and evaluate flight performance.
Wile the viability and the technologies that support it are evolving slowly,
the concept of autonomous aviation is quickly gaining speed
with sights set on a future,
where autonomous flights can provide a safer,
greener and efficient way to transport people and goods around the globe.
We will have autonomous UAVs and drones
flying to deliver cargo, to do crop inspection
to build up safety,
in terms of pipeline inspections and railway inspections.
So we will have a lot of things flying autonomously.
What you might find is because the innovation is in the smaller vehicles,
we can have more smaller vehicles rather than one big vehicle.
They can be more diverse in where they go.
I think then once cargo gets going,
I think around 2026/2028,
you'll probably have air taxis,
and you'll start to move them.
So it's going to be a slow burn.
It hinges on being able to safely demonstrate and work with the regulator
that we collectively have the technologies
to do this in a safe and efficient manner.