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Mars, and the Curiosity rover now exploring it, are 120 million miles away. Back at mission control, rover cameras show a rocky incline, maybe ten feet ahead, near the path Curiosity is supposed to travel. How does NASA make sure the precious rover doesn’t go tumbling down the small slope?
The answer involves the team of rover drivers glued to their computer screens at the Jet Propulsion Laboratory. They work the world’s most exclusive—and perhaps most high-pressure—video simulation. A virtual Curiosity can be placed into the latest images that come down from Mars, moved around to see what happens, and be driven over virtual inclines mapped out via satellite to see how it does. The set-up even comes with 3-D capability and glasses.
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The drivers work their screens with an arcade-like intensity—you almost expect them to reach for the joystick. But that’s not how you drive on Mars. It’s much more complicated than that, and the stakes could hardly be higher.
SEEING IN 3-D
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First off, there’s a 14-minute radio lag between Mars and Earth, so even with their state-of-the-art programs, the real-time driving is essentially blind. Plus, the movements of the rover need to be analyzed, checked, rechecked and sometimes changed suddenly by the dozens of engineers and scientists who plan the rover’s movements.
Based on their input, the rover drivers pick a path and then write computer sequences that will tell Curiosity how to follow it and how far to go. They do all this while the rover is asleep; the instructions are delivered when the Martian morning starts. Nobody will know what really happened with the driving until the first downlink some eight to ten hours later.
“This image just came down,” said Matt Heverly on a recent day, excited to be getting a first look from Curiosity’s new location. He’s one of the 14 “rover planners” who trained for years before getting his Mars “driver’s license,” and like most of the others he’s worked on previous Mars rover missions.
Heverly quickly placed the rover into the scene. He suggested I join him by putting on our 3-D glasses, and suddenly we’re looking at the Martian landscape as if we’re driving through the desert Southwest, minus the cacti. Before us is the next major destination: Glenelg. It’s filled with great science possibilities and more than a few potential driving pitfalls.
“We’re almost at the rim that leads down into Glenelg,” he said, eagerly scanning the horizon. “Now we have to figure out how to get where they want us to go.”
MASTERING MOVEMENTS
Watching the drivers at work brings home the fact that the fate of the $2.5 billion mission depends on how well they use their interplanetary visualizers to avoid dune fields, keep away from big rocks, stay on surfaces below the 30-degree incline the rover is designed to handle. And since they’re living on ever-changing Mars time for the mission’s first 90 days and working long, long hours, they have to do it all with a sense of perpetual jet lag.
If you think the driving is tough, consider the job of moving the arm. Seven feet long when extended, with five moving joints and topped by a heavy turret of all-important science instruments, drills and cameras, it too has to be choreographed through code to make scores of sometimes very complicated maneuvers.
Most difficult, said arm lead engineer and driver Matt Robinson, is probably going to be transporting a rock or soil sample collected at the extended turret, reconfiguring the arm so it hovers instead over the rover deck, and delicately dropping the precious sample into a one-inch opening that leads down into the chemistry labs inside Curiosity.
A key goal of the mission is to search for carbon-based organics—building blocks for life on Earth—as part of its effort to determine if Mars was ever habitable. It can only complete that task if Robinson and his colleagues master the arm movements needed to drill or scoop, collect, sift, and then deliver the Martian samples. Other rovers and landers on Mars have collected samples, but never with the level of complexity brought by Curiosity.
CURVEBALL AHEAD?
As we watched a simulated unfurling of the arm—kind of like a tai chi exercise— as it readied to work, Robinson said that his team has worked on the sampling programs for months. They’re confident they can make it work, but don’t entirely know how differently it will behave in the thin atmosphere of Mars than on Earth.
“We placed rigorous requirements on our designs, but we can’t place requirements on Mars,” he said. “We don’t know the curveballs, and that means we have to be flexible.”
So being a Curiosity driver is by all accounts a thrill, but the pressure is high. Automatic “Hazard Avoidance” driving features have been built into the rover but still, one seriously wrong move and the arm can crash into the mast holding the prized cameras shooting high-resolution color. Or even worse, the rover can take a wrong turn that leads to a very bad day. The members of the rover planning team all have their Mars driver’s licenses, but the planet hasn’t made clear yet what it makes of them.
Using an online tool to label Martian terrain types, you can train an artificial intelligence algorithm that could improve the way engineers guide the Curiosity rover.
You may be able to help NASA's Curiosity rover drivers better navigate Mars. Using the online tool AI4Mars to label terrain features in pictures downloaded from the Red Planet, you can train an artificial intelligence algorithm to automatically read the landscape.
Is that a big rock to the left? Could it be sand? Or maybe it's nice, flat bedrock. AI4Mars, which is hosted on the citizen science website Zooniverse, lets you draw boundaries around terrain and choose one of four labels. Those labels are key to sharpening the Martian terrain-classification algorithm called SPOC (Soil Property and Object Classification).
Developed at NASA's Jet Propulsion Laboratory, which has managed all of the agency's Mars rover missions, SPOC labels various terrain types, creating a visual map that helps mission team members determine which paths to take. SPOC is already in use, but the system could use further training.
'Typically, hundreds of thousands of examples are needed to train a deep learning algorithm,' said Hiro Ono, an AI researcher at JPL. 'Algorithms for self-driving cars, for example, are trained with numerous images of roads, signs, traffic lights, pedestrians and other vehicles. Other public datasets for deep learning contain people, animals and buildings — but no Martian landscapes.'
Once fully up to speed, SPOC will be able to automatically distinguish between cohesive soil, high rocks, flat bedrock and dangerous sand dunes, sending images to Earth that will make it easier to plan Curiosity's next moves.
'In the future, we hope this algorithm can become accurate enough to do other useful tasks, like predicting how likely a rover's wheels are to slip on different surfaces,' Ono said.
The Job of Rover Planners
JPL engineers called rover planners may benefit the most from a better-trained SPOC. They are responsible for Curiosity's every move, whether it's taking a selfie, trickling pulverized samples into the rover's body to be analyzed or driving from one spot to the next.
It can take four to five hours to work out a drive (which is now done virtually), requiring multiple people to write and review hundreds of lines of code. The task involves extensive collaboration with scientists as well: Geologists assess the terrain to predict whether Curiosity's wheels could slip, be damaged by sharp rocks or get stuck in sand, which trapped both the Spirit and Opportunity rovers.
Planners also consider which way the rover will be pointed at the end of a drive, since its high-gain antenna needs a clear line of sight to Earth to receive commands. And they try to anticipate shadows falling across the terrain during a drive, which can interfere with how Curiosity determines distance. (The rover uses a technique called visual odometry, comparing camera images to nearby landmarks.)
How AI Could Help
SPOC won't replace the complicated, time-intensive work of rover planners. But it can free them to focus on other aspects of their job, like discussing with scientists which rocks to study next.
'It's our job to figure out how to safely get the mission's science,' said Stephanie Oij, one of the JPL rover planners involved in AI4Mars. 'Automatically generating terrain labels would save us time and help us be more productive.'
The benefits of a smarter algorithm would extend to planners on NASA's next Mars mission, the Perseverance rover, which launches this summer. But first, an archive of labeled images is needed. More than 8,000 Curiosity images have been uploaded to the AI4Mars site so far, providing plenty of fodder for the algorithm. Ono hopes to add images from Spirit and Opportunity in the future. In the meantime, JPL volunteers are translating the site so that participants who speak Spanish, Hindi, Japanese and several other languages can contribute as well.
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Andrew Good
Jet Propulsion Laboratory, Pasadena, Calif.
818-393-2433
andrew.c.good@jpl.nasa.gov
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Grey Hautaluoma / Alana Johnson
NASA Headquarters, Washington
202-358-0668 / 202-358-1501
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