Deep drone acrobatics

Date:
June 23, 2020
Source:
University of Zurich
Summary:
A navigation algorithm enables drones to learn challenging acrobatic
maneuvers. Autonomous quadcopters can be trained using simulations
to increase their speed, agility and efficiency, which benefits
conventional search and rescue operations.



FULL STORY ========================================================================== Since the dawn of flight, pilots have used acrobatic maneuvers to
test the limits of their airplanes. The same goes for flying drones: Professional pilots often gage the limits of their drones and measure
their level of mastery by flying such maneuvers in competitions

========================================================================== Greater efficiency, full speed Working together with microprocessor
company Intel, a team of researchers at the University of Zurich has now developed a quadrotor helicopter, or quadcopter, that can learn to fly acrobatic maneuvers. While a power loop or a barrel role might not be
needed in conventional drone operations, a drone capable of performing
such maneuvers is likely to be much more efficient. It can be pushed to
its physical limits, make full use of its agility and speed, and cover
more distance within its battery life.

The researchers have developed a navigation algorithm that enables
drones to autonomously perform various maneuvers -- using nothing more
than onboard sensor measurements. To demonstrate the efficiency of
their algorithm, the researchers flew maneuvers such as a power loop,
a barrel roll or a matty flip, during which the drone is subject to
very high thrust and extreme angular acceleration. "This navigation is
another step towards integrating autonomous drones in our daily lives,"
says Davide Scaramuzza, robotics professor and head of the robotics and perception group at the University of Zurich.

Trained in simulation At the core of the novel algorithm lies an
artificial neural network that combines input from the onboard camera
and sensors and translates this information directly into control
commands. The neural network is trained exclusively through simulated
acrobatic maneuvers. This has several advantages: Maneuvers can easily
be simulated through reference trajectories and do not require expensive demonstrations by a human pilot. Training can scale to a large number of diverse maneuvers and does not pose any physical risk to the quadcopter.

Only a few hours of simulation training are enough and the quadcopter
is ready for use, without requiring additional fine-tuning using
real data. The algorithm uses abstraction of the sensory input from
the simulations and transfers it to the physical world. "Our algorithm
learns how to perform acrobatic maneuvers that are challenging even for
the best human pilots," says Scaramuzza.

Fast drones for fast missions However, the researchers acknowledge that
human pilots are still better than autonomous drones. "Human pilots can
quickly process unexpected situations and changes in the surroundings,
and are faster to adjust," says Scaramuzza.

Nevertheless, the robotics professor is convinced that drones used for
search and rescue missions or for delivery services will benefit from
being able to cover long distances quickly and efficiently.

Video: https://www.youtube.com/watch?v=2N_wKXQ6MXA&feature=youtu.be

========================================================================== Story Source: Materials provided by University_of_Zurich. Note: Content
may be edited for style and length.


========================================================================== Journal Reference:
1. Elia Kaufmann, Antonio Loquercio, Rene' Ranftl, Matthias Mu"ller,
Vladlen
Koltun, Davide Scaramuzza. Deep Drone Acrobatics. Robotics:
Science and Systems, 2020 [link] ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/06/200623145341.htm

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