for finding people under snow. The device emits a radio
beacon a rescue team can pick up using another ARVA
receiver device. The latter essentially operates as a direction finding device, generating a “U-turn” signal whenever
it detects the person carrying it starts moving away from
the emitter. Modern ARVA devices are able to reach a 5m
accuracy in locating an emitter under 10m of snow. 20
Our goal is to control the drone so that it reaches
the supposed location of an ARVA emitter. To that end,
we integrate a Pieps DSP PRO20 ARVA receiver with the
Pixhawk board. A custom PID controller aligns the
drone’s yaw with the direction pointed by the on-board
ARVA receiver. Roll and pitch, instead, are determined
to fly at constant speed along the direction indicated by
the ARVA receiver. Navigation is thus entirely determined
by the ARVA inputs. We implement this controller both
using reactive control by probing the ARVA device as fast
as possible, and with time-triggered control at 400Hz,
that is, the same as in the original time-triggered implementation of Ardupilot.
We place an ARVA transmitter at one end of Rugby,
and set up the quadcopter at 100m distance facing opposite to it. Even though GPS does not provide any inputs
for navigation, we use it to track the path until the first
time the ARVA device generates the “U-turn” signal. We
compare the duration and length of the flight when using
reactive or time-triggered control. We repeat this experiment 20 times in comparable environmental conditions.
Results. Reactive control results in a 21% (11%) reduction in the duration (length) of the flight, on average.
Time-triggered control also shows higher variance in the
results, occasionally producing quite inefficient paths.
Figure 9 shows an example. The path followed by reactive
control appears fairly smooth. In contrast, time-triggered
control shows a convoluted trajectory at about one-third
of the distance, where the yaw is almost ± 90° compared to
the target.
The logs we collect during the experiment indicate that
the reason for this behavior is essentially the inability of
time-triggered control to promptly react. Probably because
of a sudden wind gust, at some point the drone gains a lateral momentum. Time-triggered control is unable to react
fast enough; a higher than 400Hz rate would probably be
Figure 9. Example of ARVA-driven navigation when using reactive
(black) and time-triggered control (yellow). Time-triggered control
occasionally produces highly inefficient paths, whereas we never
observe similar behaviors with reactive control.
needed in this case, and the drone turns almost 90°. We
never observe this behavior with reactive control, which
better manages available processing resources against
environment influences.
6. CONCLUSION AND OUTLOOK
Reactive control replaces the traditional time-triggered
implementation of drone autopilots by governing the
execution of the control logic based on changes in the
navigation sensors. This allows the system to dynamically
adapt the control rate to varying environment dynamics. To that end, we conceived a probabilistic approach
to trigger the execution of the control logic, a way to
carefully regulate the control executions over time, and
an efficient implementation on resource-constrained
embedded hardware. The benefits provided by reactive
control include higher accuracy in motion control and
longer flight times.
We are currently working toward obtaining official
certifications from the Italian civil aviation authority to
fly drones running reactive control over public ground.
Surprisingly, the major hampering factor is turning
out not to be reactive control per se. The evidence we
collected during our experiments, plus (i) additional
fallback mechanisms we implement to switch back
to time-triggered control in case of problems and (ii)
extensive tests conducted by independent technicians
and professional pilots, were sufficient to convince the
authority on the efficient and dependable operation of
reactive control.
Rather, the authority would like to obtain a precise
specification of what kind of drone, intended in its physical parts, can support reactive control. In computing
terms, this essentially means a specification of the target platform. This task represents a multi-disciplinary
challenge, in that it requires skills and expertise beyond
the computing domain and reaching into electronics,
aeronautics, and mechanics. We believe much of the
future of computer science rests here, at the confluence
with other disciplines.
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Figure 8. Average rate of control at second scale in two example
Ardupilot runs. Reactive control adapts the rate of control executions
both in the short and long term, and according to the perceived
environment influence.