Writing for The Conversation, Nick Martin, Senior Lecturer in
Aerodynamics in the Department of Mechanical and Construction Engineering, looks at how artificial intelligence
could use lessons from nature to help drones stay in the air for longer.
Birds have
long inspired humans to create their own ways to fly. We know that soaring bird
species that migrate long distances use thermal updrafts to stay in the air
without using up energy flapping their wings. And glider pilots similarly use
thermals currents and other areas of rising air in order to remain airborne for
longer.
Yet, while
we’ve mastered gliding through these updrafts using various instruments, the
exact mechanisms that allow birds to soar are still unknown. But a team of
researchers from California and Italy have made some telling steps towards
answering this question using artificial intelligence (AI). And it could lead
to new developments in navigation systems for aircraft, with particular
implications for creating drones that can stay airborne for very long periods
of time.
The aim of
the study, published in Nature, was to train a small
two-metre wingspan autonomous glider to fly in thermals, just like a real bird
would. The glider was programmed with a kind of AI known as machine learning
that enabled it to work out how to use the air currents to stay in the air for
longer.
Machine learning is an alternative approach to
programming a computer to do a complex task. Rather than feeding a computer (or
autonomous glider in this case) a set of instructions telling it how to do
something, you tell the computer how you would like it to respond and reward it
when it does the right thing.
Over time
it will learn what things are rewarded and will tend to do these behaviours instead.
This technique is how computer programs such as Google’s AlphaGo can learn to play the board
game Go and then beat professional players, a feat simply not possible with
conventional programming techniques.
This type
of machine learning is called reinforcement learning and it relies on a
large amount of input data being fed to the computer in order for it to learn
what actions will provide it with rewards. For the researchers programming the
autonomous glider, the input data consisted of specialised instruments capable
of reading the change in upwards (vertical) wind strength. The instruments were
able to determine these changes along the length of the glider (longitudinally)
and from one wing tip to the other (laterally). The sensors were able to make
these measurements ten times every second.
This data
was then used to make flight adjustments to what is known as the bank angle of
the of the glider. A well-balanced aeroplane with its wings level has zero bank
angle and will fly in a straight line. Tilting the wings and increasing the
bank angle will make the plane turn. In the study, the glider was rewarded if
the change in upward wind speed along its flight path increased. In other
words, if the glider was flying into an updraft.
Updrafts
are key to increasing the amount of time a glider can stay airborne. Unlike a
powered aeroplane, a glider not able to find any updrafts will gradually fall
toward the ground. Whether or not the glider is falling or rising depends
directly upon how much air is moving upwards around it. In an updraft, the
increase in vertical air movement can be enough to stop the glider falling and,
if the vertical wind is strong enough, allow it to climb.
Over the
course of a number of flights (about 16 hours of flying in total), the study
glider learnt to fly by training itself that under a certain combination of
inputs (bank angle, longitudinal and lateral change vertical wind speed) to
decide what the next change in bank angle should be. The result was that by the
end of all that flying the plane had taught itself how to fly into updrafts,
allowing it to stay in the air for longer.
As a
bonus, the researchers used a numerical model to show this approach would
benefit larger gliders even more, since their longer wingspan will provide a
more accurate measurement of the change in upwards wind speed from one wing tip
to another.
Making aircraft smarter
The
results raise the question of what possible futuristic autonomous gliders we
could see gliding around and what would they be used for. Engineers at MIT
recently took inspirationfrom the aerodynamics of the
wave-riding albatross to design an autonomous glider.
Airbus has
developed a solar-powered glider that can remain airborne for very long periods
of time as an alternative to surveillance or communications satellites, for
example that could broadcast internet signals to remote locations on the
ground. Microsoft is reportedly working on autonomous planes with
state-of-the-art artificial intelligent navigation systems.
But
perhaps the techniques developed in this study could one day lead to a new
generation of “smart” navigation and autopilot systems for conventional
aircraft. These could use data gathered over thousands of hours of flying time
to make decision about the most efficient way to get around. This would rely on
accurate sensors and further development that would allow a plane to identify
and then hop from one thermal updraft to another. At the moment, the method
only allows gliding inside a single thermal.
The
methods and programming techniques developed in by the researchers will
undoubtedly bring us a step closer to the goal of an autonomous flying vehicle
with flight times of days, weeks or months carrying out these tasks. But it is
the use of reinforcement learning that once again shows how flexible these
algorithms are at adapting to a wide range of complex tasks, from controlling a
glider to beating a human at Go.
You can read the original article in
The Conversation.
