Can a Robot Draw a Map?
Atop a small rise in the landscape, in the shade of a six-foot-square canopy, a quartet of researchers sit in folding chairs, portable computers on their laps, alternately scanning screens of status data transmitted by Zoë, discussing the robot’s behavior, and writing on-the-spot patches to its control software.
Zoë carries with it, on a hard disk, a crude map of the region it’s exploring, based on data collected by ASTER, an infrared spectrometer onboard NASA’s Earth Observing Satellite. Zoë’s goal is to produce a more-detailed map that clearly defines the boundaries between areas of the terrain dominated by clay and those dominated by basalt. It would be a relatively simple matter for a field geologist to do this work. But what the Carnegie Mellon University researchers who are part of this Science on the Fly experiment want to know is: can a robot can do it too?
To members of the CMU team, it’s obvious that the crumbly brown stuff is clay and the hard black stuff is basalt. Color and texture make the distinction clear in an instant. The researchers have purposely picked a simple landscape for their test, dominated by two distinct rock types. Many geologic settings are more complex, but they wanted to give Zoë a relatively clear task on its first full field trial. If Zoë can learn to perform its task here, one day a more-refined version of its software could be used on a robotic mission to Mars, where geologists are not likely to be wandering about any time soon.
On each of its traverses, Zoë attempts to build a detailed map of an area ranging from 30,000 to 75,000 square meters (about 7.5 to 18.5 acres). The robot can’t cover the entire area; rather it winds along a narrow path of its own choosing, up to a kilometer (six-tenths of a mile) long, moving about one meter, and taking one spectral image, per second. Each time it captures an image, it rebuilds its map. It analyzes every pixel, identifying it as either basalt, or clay, or still-unknown, and assigning a confidence level to its classification. Then it decides where to go next. (Zoë doesn’t actually distinguish between “basalt” and “clay”; it knows only that they are two different classes of rocks, with different spectral signatures. It needs a geologist to decide what types of rock they are. )
The hard part is choosing the most effective path, the path that will yield the greatest amount of information. Ideally, Zoë wants to go directly to the place it knows least about. But there are some obstacles. For one thing, the most ambiguous part of the map may be far away. So the robot needs to figure out how to do useful science on its way to getting there.
Zoë also has to avoid obstacles, such as steep hills – and bushes. When the CMU team scouted the Amboy Crater area, there were some creosote bushes scattered about, but they were dry and leafless – from Zoë’s point of view, too insubstantial to pay attention to. Then it rained, and the bushes leafed out, making them more formidable obstacles, which Zoë had to be taught to drive around.
Wettergreen is pleased with the success of the Science on the Fly field test. In an email after the field work wrapped up, he wrote that, although a great deal of development remained to be done before software like Zoë’s could be used to guide a rover on Mars, the “system demonstrated many of the ‘common sense’ intuitions that field geologists take for granted: spatial awareness, the ability to correlate surface and orbital views, [and] a tendency to seek novel data.”
One aspect of Zoë’s software, however, is closer to being ready for prime time: the use of far-field sensing – looking off into the distance, rather than looking just a few feet ahead – to help figure out how to avoid obstacles. Far-field sensing may seem like a no-brainer to us – we do it all the time – but it actually involves quite a bit of brainpower. That’s what makes it challenging for a robot. Nevertheless, says Wettergreen, he and his colleagues now “have proven the concept.” What remains is “to test, test, test.”
The Science on the Fly project was funded by NASA’s ASTEP (Astrobiology Science and Technology for Exploring Planets) program.