IEEE Spectrum October, 2013 - 31

The U.s. navy's carrier-based copter, the Northrop Grumman Fire Scout [far left],
can fly itself onto and off of a moving deck. The U.S. Marine Corps's K-Max ferries cargo
to soldiers, dangling a "sling load" to work around its weakness in landing on rough
ground. To rescue people, however, a robocopter needs better eyes and judgment.

One experimental unmanned helicop‑
ter nearly landed on a boulder and had
to be saved by the backup pilot. Another
recently crashed during the landing
phase. To avoid such embarrassments,
the K-Max dangles cargo from a rope as a
"sling load" so that the helicopter doesn't
have to land when making a delivery.
Such work‑arounds throw away much
of the helicopter's inherent advantage.
If we want these machines to save lives,
we must give them eyes, ears, and a modi‑
cum of judgment.
In other words, an autonomous sys‑
tem needs perception, planning, and
control. It must sense its surroundings
and interpret them in a useful way.
Next, it must decide which actions to
perform in order to achieve its objec‑
tives safely. Finally, it must control itself
so as to implement those decisions.
A cursory search on YouTube will un‑
cover videos of computer‑controlled
miniature quadcopters doing flips, slip‑

ping vertically through slots in a wall,
and assembling structures. What these
craft are missing, though, is perception:
They perform inside the same kind of
motion‑ capture lab that Hollywood
uses to record actors' movements for
computer graphics animations. The po‑
sition of each object is precisely known.
The trajectories have all been computed
ahead of time, then checked for errors
by software engineers.
If you give such a quadcopter on‑
board sensors and put it outdoors, away
from the rehearsed dance moves of the
lab, it becomes much more hesitant. Not
only will it sense its environment rather
poorly, but its planning algorithms will
barely react in time when confronted
with an unusual development.
True, improvements in hardware are
helping small quadcopters approach full
autonomy, and somewhat larger model
helicopters are already quite far along in
that quest. For example, several groups
have shown capabilities such as auto‑
mated landing, obstacle avoidance, and
mission planning on the Yamaha RMax,
a 4‑meter machine originally sold for
remote‑control crop dusting in Japan's
hilly farmlands. But this technology
doesn't scale up well, mainly because
the sensors can't see far enough ahead
to manage the higher speeds of full‑size
helicopters. Furthermore, existing soft‑
ware can't account for the aerodynamic
limitations of larger craft.
Another problem with the larger heli‑
copters is that they don't actually like
to hover. A helicopter typically lands
more like an airplane than most peo‑
ple realize, making a long, descending
approach at a shallow angle at speeds
of 40 knots (75 kilometers per hour) or
more and then flaring to a hover and ver‑
tical descent. This airplane‑like profile is
necessary because hovering sometimes
requires more power than the engines
can deliver.

T

h e n e e d f o r fa s t f ly i n g h a s a l o t t o
do with the challenges of perception
and planning. We knew that making
large, autonomous helicopters practical
would require sensors with longer rang‑
es and faster measurement rates than
had ever been used on an autonomous
rotary aircraft, as well as software opti‑
mized to make quick decisions. To solve
the first problem, we began with ladar-
laser detection and ranging-a steadily
improving technology and one that's
already widely used in robotic vehicles.
Ladar measures the distance to objects
by first emitting a tightly focused laser
pulse and then measuring how long it
takes for any reflections to return. It cre‑
ates a 3-D map of the surroundings by
pulsing 100 000 times per second, steer‑
ing the beam to many different points
with mirrors, and combining the results
computationally.
The ladar system we constructed for
the ULB uses a "nodding" scanner. A
"fast‑axis" mirror scans the beam in a
horizontal line up to 100 times per sec‑
ond while another mirror nods up and
down much more slowly. To search for
a landing zone, the autonomous system
points the ladar down and uses the fast‑
axis line as a "push broom," surveying
the terrain as the helicopter flies over it.
When descending nearer to a possible
landing site, the system points the ladar
forward and nods up and down, thus
scanning for utility wires or other low‑
lying obstacles.
Because the helicopter is moving, the
ladar measures every single point from
a slightly different position and angle.
Normally, vibration would blur these
measurements, but we compensate for
that problem by matching the scanned
information with the findings of an iner‑
tial navigation system, which uses GPS,
accelerometers, and gyros to measure
position within centimeters and angles
within thousandths of a degree. That

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Table of Contents for the Digital Edition of IEEE Spectrum October, 2013
