Technology | DOI: 10.1145/1941487.1941494
Gregory Goth
I, Domestic Robot
With recent advances in laser rangefinders, faster algorithms,
and open source robotic operating systems, researchers are increasing
domestic robots’ semantic and situational awareness.
InDUstrIaL roBots, fIXeD-Lo- CatIon and single-function machines, have long been sta- ples of advanced manufactur- ing settings. Medical robots,
which can help surgeons operate with
smaller incisions and cause less blood
loss than traditional surgical methods,
are making fast inroads in metropolitan and suburban hospitals. Rescue robots, included wheeled and snake-like
robots, are increasingly common, and
were deployed in the search for survivors in the aftermath of the earthquake
and tsunami that recently struck Japan. On the other hand, the promise of
multipurpose domestic assistance robots, capable of a wide range of tasks,
has been a distant goal.
However, recent advances in hardware such as laser rangefinders, open
source robotic operating systems, and
faster algorithms have emboldened researchers. Robots are now capable of
folding laundry, discerning where to
place an object on cluttered surfaces,
and detecting the presence of people
in a typical room setting.
“It’s easy for me to be optimistic,
but if robots aren’t actually being useful and fairly widespread in 10 years,
then I will be fairly disappointed,” says
Charles Kemp, assistant professor of
biomedical engineering at Georgia
Tech University.
sensors enable awareness
In recent months, numerous research
teams have published papers detailing advances in robots’ perceptual capabilities. These perceptual advances
enable the robots’ mechanical components to complete domestic tasks hitherto impossible.
Kemp and his research team have
pioneered semantic and situational
awareness in robots through several
methods, including the creation of
radio frequency identification (RFID)
Willow Garage’s PR2, an open source
robotics research and development platform.
semantic tags on common objects
such as light switches, and by combining sensor data taken from both two-dimensional camera data and three-dimensional point clouds gathered by
laser rangefinders.
University of Bonn researchers Jörg
Stückler and Sven Behnke also demonstrated success, using a combination of 2D laser and camera sensors.
They programmed a mobile service
robot to combine laser rangefinder
data that hypothesizes the presence of
a person’s legs and torso with 2D frontal and profile images of the detected
face.
Stückler and Behnke also modeled the semantic probability of detecting a person’s presence in different locations of a room—high
probability in a chair and low probability on a bookshelf, for instance—
and supplied the robot with that
knowledge. The prior knowledge of
the room semantics and precalculat-
ed range of likely valid facial height
helps the Bonn researchers discern
false positive returns.
Steve Cousins, CEO of Willow Garage, which manufactures the open
platform general-purpose PR2 robot,
says further advances in perceptual capabilities may be even more likely with
the recent debut of sensing technology
that enables a computer to analyze an
area in three dimensions and then to
create what the technology’s manufacturer, PrimeSense, calls a synchronized
depth image. The technology sells for
less than 1/20th of the de facto standard research rangefinder, which costs
about $5,000. Both Cousins and Kemp
believe the low cost of the PrimeSense
sensor (it is a key component of Microsoft’s Kinect gaming system) may lead
to a surge in situational and semantic
robotic research. Kemp says his team
recently installed one of the new sensors to its PR2.
In essence, Kemp says its real-time
technology greatly simplifies a robot’s
data-gathering process.
Prior to installing the new sensor,
on projects such as the work on making the robot discern clutter, he says
“we had to tilt the laser rangefinder
up and down, then snap a picture and
relate those two things. That’s a pretty
slow process and really expensive.”
a semantic Database
Kemp says there are two distinct research areas for similar problem sets
in domestic robotics: those related to
perceptual problem sets, and those
related to mechanical awareness. For
example, a roving robot meant to help
a person with basic housekeeping
chores must not only know how to differentiate a refrigerator door handle
from a light switch, but it must also be
able to calculate which approach its
arms must take, and how firmly it must
grip the respective levers.