Tech Briefs Magazine - September 2022 - 35

Teaching Drones How to Navigate Real-World Weather
A deep-learning method that can help drones cope with new and unknown wind conditions.
Caltech, Pasadena, CA
R
ight now, drones are either flown under
controlled conditions, with no
wind, or are operated by humans using
remote controls. However, for drones to
autonomously perform necessary but
quotidian tasks, such as delivering packages
or airlifting injured drivers from a
traffic accident, drones must be able to
adapt to wind conditions in real time.
To face this challenge, a team of engineers
from Caltech has developed Neural-Fly,
a deep-learning method that can
help drones cope with new and unknown
wind conditions in real time just by updating
a few key parameters. Neural-Fly
is described in a study published in Science
Robotics.
Neural-Fly was tested at Caltech's Center
for Autonomous Systems and Technologies
(CAST) using its Real Weather
Wind Tunnel, a custom 10-foot-by-10foot
array of more than 1,200 tiny computer-controlled
fans that allows engineers
to simulate everything from a light
gust to a gale.
" The issue is that the direct and specific
effect of various wind conditions on aircraft
dynamics, performance, and stability
cannot be accurately characterized as a
simple mathematical model, " said Soon-Jo
Chung, Bren Professor of Aerospace and
Control and Dynamical Systems and Jet
Propulsion Laboratory Research Scientist.
" Rather than try to qualify and quantify
each and every effect of turbulent and unpredictable
wind conditions we often experience
in air travel, we instead employ a
combined approach of deep learning and
adaptive control that allows the aircraft to
learn from previous experiences and
adapt to new conditions on the fly with stability
and robustness guarantees. "
Neural-Fly gets around these challenges
by using a so-called separation strategy,
through which only a few parameters of
the neural network must be updated in
real time.
After obtaining as little as 12 minutes of
flying data, autonomous quadrotor
drones equipped with Neural-Fly learn
how to respond to strong winds so well
that their performance significantly improved
(as measured by their ability to
precisely follow a flight path). The error
rate following that flight path is up to 2.5
times to 4 times smaller compared to
Tech Briefs, September 2022
Time-lapse photo shows a drone equipped with Neural-Fly maintaining a figure-eight course amid
stiff winds at Caltech's Real Weather Wind Tunnel. (Image: Caltech)
Engineers test Neural-Fly in the open air on Caltech's campus. (Image: Caltech)
drones equipped with the current state of
the art adaptive control algorithms that
identify and respond to aerodynamic effects
but without deep neural networks.
Though landing might seem more
complex than flying, Neural-Fly, unlike
the earlier systems, can learn in real
time. It can respond to changes in wind
on the fly, and it does not require tweaking
after the fact.
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Neural-Fly performed as well in flight
tests conducted outside the CAST facility
as it did in the wind tunnel. Further, the
team has shown that flight data gathered
by an individual drone can be transferred
to another drone, building a pool
of knowledge for autonomous vehicles.
For more information, contact Robert
Perkins at rperkins@caltech.edu; 626395-1862.
35

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