Tech Briefs Magazine - December 2021 - 24

Aeronautics
System Trains Drones to Fly Around Obstacles at
High Speeds
The new algorithm could enable fast, nimble drones for time-critical operations such as
search and rescue.
Massachusetts Institute of Technology, Cambridge
he faster drones fly, the more unstable
they become and at high speeds,
their aerodynamics can be too complicated
to predict. Crashes, therefore, are
a common occurrence. Faster and more
nimble drones could be put to use in
time-critical operations; for instance, to
search for survivors in a natural disaster.
Aerospace engineers devised an algorithm
that helps drones find the fastest
route around obstacles without crashing.
The new algorithm combines simulations
of a drone flying through a virtual
obstacle course with data from experiments
of a real drone flying through the
same course in a physical space. The
researchers found that a drone trained
with the algorithm flew through a simple
obstacle course up to 20 percent faster
than a drone trained on conventional
planning algorithms.
Training drones to fly around obstacles
is relatively straightforward if they
are meant to fly slowly. That's because
aerodynamics such as drag don't generally
come into play at low speeds and
they can be left out of any modeling of a
drone's behavior. But at high speeds,
such effects are far more pronounced
and how the vehicles will handle is much
harder to predict.
T
There could be delays in sending a signal
to a motor or a sudden voltage drop
that could cause other dynamics problems.
These effects can't be modeled
with traditional planning approaches.
To get an understanding for how
high-speed aerodynamics affect drones
in flight, researchers have to run many
experiments in the lab, setting drones at
various speeds and trajectories to see
which fly fast without crashing - an
expensive and often crash-inducing
training process. Instead, the team
developed a high-speed flight-planning
algorithm that combines simulations
and experiments in a way that minimizes
the number of experiments required to
identify fast and safe flight paths.
The researchers started with a physicsbased
flight planning model, which they
developed to first simulate how a drone
is likely to behave while flying through a
24
Cov
A quadcopter flies a racing course through several gates to find the fastest feasible trajectory.
(Courtesy of the researchers)
virtual obstacle course. They simulated
thousands of racing scenarios, each with
a different flight path and speed pattern.
They then charted whether each
scenario was feasible (safe) or infeasible
(resulting in a crash). From this chart,
they could quickly zero in on a handful
of the most promising scenarios, or racing
trajectories, to try out in the lab.
To demonstrate their new approach,
the researchers simulated a drone flying
through a simple course with five large,
square-shaped obstacles arranged in a
staggered configuration. They set up
this same configuration in a physical
training space and programmed a drone
to fly through the course at speeds and
trajectories that they previously picked
out from their simulations. They also
ran the same course with a drone
trained on a more conventional algorithm
that does not incorporate experiments
into its planning.
Overall, the drone trained on the
new algorithm completed the course in
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ToC
a shorter time than the conventionally
trained drone. In some scenarios, the
winning drone finished the course 20
percent faster than its competitor, even
though it took a trajectory with a slower
start; for instance, taking a bit more
time to bank around a turn. This kind
of subtle adjustment was not taken by
the conventionally trained drone; likely
because its trajectories, based solely
on simulations, could not entirely
account for aerodynamic effects that
the team's experiments revealed in the
real world.
The team plans to fly more experiments
at faster speeds and through
more complex environments to further
improve their algorithm. They also may
incorporate flight data from human
pilots who race drones remotely and
whose decisions and maneuvers might
help zero in on even faster yet still feasible
flight plans.
For more information, contact Abby
Aba zorius at abbya@mit.edu; 617-253-2709.
Tech Briefs, December 2021

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Tech Briefs Magazine - December 2021

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