One of two Slocum gliders owned and operated by the USC Center for Integrated Networked Aquatic PlatformS (CINAPS). Photo: Smith et al. |
Concerns
about the spread of radiation from damaged Japanese nuclear reactors—even as
scientists are still trying to assess the consequences of the year-old
Deepwater Horizon oil spill—have provided a painful reminder of just how
important environmental monitoring can be. But collecting data on large
expanses of land and sea can require massive deployments of resources.
At
the Institute of Electrical and Electronics Engineers’
International Conference on Robotics and Automation in May, 2011, MIT
researchers will present a new algorithm enabling sensor-laden robots to focus
on the parts of their environments that change most frequently, without losing
track of the regions that change more slowly. At the same conference, they’ll
present a second paper describing a test run of the algorithm on underwater
sensors that researchers at the Univ. of Southern California (USC) are using to
study algae blooms.
The
work of Daniela Rus, a professor of computer science and electrical
engineering, and postdocs Mac Schwager and Stephen Smith (now an assistant
professor at the Univ. of Waterloo in Ontario),
the algorithm is designed for robots that will be monitoring an environment for
long periods of time, tracing the same routes over and over. It assumes that
the data of interest—temperature, the concentration of chemicals, the presence
of organisms—fluctuate at different rates in different parts of the
environment. In ocean regions with strong currents, for instance, chemical
concentrations might change more rapidly than they do in more sheltered areas.
Floor it
In its current version, the algorithm assumes that researchers already have a
mathematical model of the rates at which conditions change in different parts
of the environment. The algorithm simply determines how the robots should
adjust their velocities as they trace their routes. For instance, given
particular rates of change along a route, would it make more sense to make one
pass in an hour, slowing down considerably in areas of frequent change, or to
make four or five passes, collecting less detailed data but taking more regular
samples?
“From
a practical point of view, it seems like an easy problem,” says Calin Belta, an
assistant professor of mechanical engineering, systems engineering and
bioinformatics at Boston
Univ., who was not
involved in the research. But it turns out to be a monstrously complex
calculation. “It’s very hard to come up with a mathematical proof that you can
really optimize the acquired knowledge,” he adds.
The
MIT researchers draw an analogy with dust accumulating on a floor—dust that’s
cleared whenever a sensor passes nearby. Because environmental change occurs at
different rates in different areas, the dust piles up unevenly. The researchers
were able to show that, with their algorithm, the height of the piles of dust
would never exceed some limit: Only so much change could occur in any area
before the sensor would measure it.
Ups and downs
Although the MIT researchers’ algorithm is designed to control robots’
velocity, the first robots on which it was tested don’t actually have velocity
controllers. USC researchers have been studying harmful algae blooms using
commercial robotic sensors designed by the Massachusetts company Webb Research. Because
the sensors are intended to monitor ocean environments for weeks on end, they
have to use power very sparingly, so they have no moving parts. Each sensor is
shaped like an airplane, with an inflatable bladder on its nose. When the bladder
fills, the sensor rises to the surface of the ocean; as the bladder empties,
the sensor glides downward.
The
more rapidly the bladder fills and empties, the steeper the sensor’s trajectory
up and down, and the longer it takes to traverse a given distance—so it’s
possible to concentrate the sensor’s attention in a particular location.
Working with colleagues in the USC computer science department, the MIT team
developed an interface that allows ocean researchers to specify regions of
interest by drawing polygons around them on a digital map and indicating their
priority with a numerical rating. The new algorithm then determines a
trajectory for the sensor that will maximize the amount of data it collects in
high-priority regions, without neglecting lower-priority regions.
At
the moment, the algorithm depends on either some antecedent estimate of rates
of change for an environment or researchers’ prioritization of regions. But in
principle, a robotic sensor should be able to deduce rates of change from its
own measurements, and the MIT researchers are currently working to modify the
algorithm so that it can revise its own computations in light of new evidence. “That’s going to be a hard problem as well,” Belta says. “But they have the
right background, and they’re strong, so I think they might be able to do it.”
The
researchers also envision that the algorithm could prove useful for fleets of
robots performing tasks other than environmental monitoring, such as tending
produce, or—in a more literal application of the vacuuming-dust metaphor—cleaning
up environmental hazards, such as oil leaking from underwater wells.
