Rapid adaptation of deep learning teaches drones to survive any weather


Date:
May 5, 2022
Source:
California Institute of Technology
Summary:
Neural-Fly technology could one day build the future of package
delivery drones and flying cars.



FULL STORY ==========================================================================
To be truly useful, drones -- that is, autonomous flying vehicles --
will need to learn to navigate real-world weather and wind conditions.


========================================================================== Right now, drones are either flown under controlled conditions, with
no wind, or are operated by humans using remote controls. Drones have
been taught to fly in formation in the open skies, but those flights
are usually conducted under ideal conditions and circumstances.

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 -- rolling with the punches, meteorologically speaking.

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 on May 4 in Science
Robotics. The corresponding author is Soon-Jo Chung, Bren Professor of Aerospace and Control and Dynamical Systems and Jet Propulsion Laboratory Research Scientist. Caltech graduate students Michael O'Connell (MS '18)
and Guanya Shi are the co-first authors.

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-10- foot 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," Chung
says. "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." O'Connell adds: "We have many different models
derived from fluid mechanics, but achieving the right model fidelity and
tuning that model for each vehicle, wind condition, and operating mode is challenging. On the other hand, existing machine learning methods require
huge amounts of data to train yet do not match state-of-the-art flight performance achieved using classical physics-based methods. Moreover,
adapting an entire deep neural network in real time is a huge, if not
currently impossible task." Neural-Fly, the researchers say, 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.

"This is achieved with our new meta-learning algorithm, which pre-trains
the neural network so that only these key parameters need to be updated
to effectively capture the changing environment," Shi says.

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 around 2.5 times to 4 times smaller
compared to the current state of the art drones equipped with similar
adaptive control algorithms that identify and respond to aerodynamic
effects but without deep neural networks.

Neural-Fly, which was developed in collaboration with Caltech's
Yisong Yue, Professor of Computing and Mathematical Sciences,
and Anima Anandkumar, Bren Professor of Computing and Mathematical
Sciences, is based on earlier systems known as Neural-Lander and
Neural-Swarm. Neural-Lander also used a deep- learning method to track
the position and speed of the drone as it landed and modify its landing trajectory and rotor speed to compensate for the rotors' backwash from
the ground and achieve the smoothest possible landing; Neural- Swarm
taught drones to fly autonomously in close proximity to each other.

Though landing might seem more complex than flying, Neural-Fly, unlike
the earlier systems, can learn in real time. As such, it can respond to
changes in wind on the fly, and it does not require tweaking after the
fact. 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.

At the CAST Real Weather Wind Tunnel, test drones were tasked with
flying in a pre-described figure-eight pattern while they were blasted
with winds up to 12.1 meters per second -- roughly 27 miles per hour,
or a six on the Beaufort scale of wind speeds. This is classified as a
"strong breeze" in which it would be difficult to use an umbrella. It
ranks just below a "moderate gale," in which it would be difficult to
move and whole trees would be swaying. This wind speed is twice as fast
as the speeds encountered by the drone during neural network training,
which suggests Neural-Fly could extrapolate and generalize well to unseen
and harsher weather.

The drones were equipped with a standard, off-the-shelf flight control
computer that is commonly used by the drone research and hobbyist
community. Neural-Fly was implemented in an onboard Raspberry Pi 4
computer that is the size of a credit card and retails for around $20.


========================================================================== Story Source: Materials provided by
California_Institute_of_Technology. Original written by Robert
Perkins. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Michael O'Connell, Guanya Shi, Xichen Shi, Kamyar Azizzadenesheli,
Anima
Anandkumar, Yisong Yue, Soon-Jo Chung. Neural-Fly enables rapid
learning for agile flight in strong winds. Science Robotics, 2022;
7 (66) DOI: 10.1126/scirobotics.abm6597 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/05/220505085644.htm

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