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Preparations for Next Moonwalk Simulations Underway (and Underwater)

In this time-lapse video of a test conducted at JPL in June 2023, an engineering model of the Planetary Instrument for X-ray Lithochemistry (PIXL) instrument aboard NASA’s Perseverance Mars rover places itself against a rock to collect data.
NASA/JPL-Caltech

Artificial intelligence is helping scientists to identify minerals within rocks studied by the Perseverance rover.

Some scientists dream of exploring planets with “smart” spacecraft that know exactly what data to look for, where to find it, and how to analyze it. Although making that dream a reality will take time, advances made with NASA’s Perseverance Mars rover offer promising steps in that direction.

For almost three years, the rover mission has been testing a form of artificial intelligence that seeks out minerals in the Red Planet’s rocks. This marks the first time AI has been used on Mars to make autonomous decisions based on real-time analysis of rock composition.

PIXL Viewed on Mars
PIXL, the white instrument at top left, is one of several science tools located on the end of the robotic arm aboard NASA’s Perseverance rover. The Mars rover’s left navcam took the images that make up this composite on March 2, 2021
NASA/JPL-Caltech

The software supports PIXL (Planetary Instrument for X-ray Lithochemistry), a spectrometer developed by NASA’s Jet Propulsion Laboratory in Southern California. By mapping the chemical composition of minerals across a rock’s surface, PIXL allows scientists to determine whether the rock formed in conditions that could have been supportive of microbial life in Mars’ ancient past.

Called “adaptive sampling,” the software autonomously positions the instrument close to a rock target, then looks at PIXL’s scans of the target to find minerals worth examining more deeply. It’s all done in real time, without the rover talking to mission controllers back on Earth.

“We use PIXL’s AI to home in on key science,” said the instrument’s principal investigator, Abigail Allwood of JPL. “Without it, you’d see a hint of something interesting in the data and then need to rescan the rock to study it more. This lets PIXL reach a conclusion without humans examining the data.”

rock target nicknamed “Thunderbolt Peak”
This image of a rock target nicknamed “Thunderbolt Peak” was created by NASA’s Perseverance Mars rover using PIXL, which determines the mineral composition of rocks by zapping them with X-rays. Each blue dot in the image represents a spot where an X-ray hit.
NASA/JPL-Caltech/DTU/QUT

Data from Perseverance’s instruments, including PIXL, helps scientists determine when to drill a core of rock and seal it in a titanium metal tube so that it, along with other high-priority samples, could be brought to Earth for further study as part of NASA’s Mars Sample Return campaign.

Adaptive sampling is not the only application of AI on Mars. About 2,300 miles (3,700 kilometers) from Perseverance is NASA’s Curiosity, which pioneered a form of AI that allows the rover to autonomously zap rocks with a laser based on their shape and color. Studying the gas that burns off after each laser zap reveals a rock’s chemical composition. Perseverance features this same ability, as well as a more advanced form of AI that enables it to navigate without specific direction from Earth. Both rovers still rely on dozens of engineers and scientists to plan each day’s set of hundreds of individual commands, but these digital smarts help both missions get more done in less time.

“The idea behind PIXL’s adaptive sampling is to help scientists find the needle within a haystack of data, freeing up time and energy for them to focus on other things,” said Peter Lawson, who led the implementation of adaptive sampling before retiring from JPL. “Ultimately, it helps us gather the best science more quickly.”

Using AI to Position PIXL

AI assists PIXL in two ways. First, it positions the instrument just right once the instrument is in the vicinity of a rock target. Located at the end of Perseverance’s robotic arm, the spectrometer sits on six tiny robotic legs, called a hexapod. PIXL’s camera repeatedly checks the distance between the instrument and a rock target to aid with positioning.

Temperature swings on Mars are large enough that Perseverance’s arm will expand or contract a microscopic amount, which can throw off PIXL’s aim. The hexapod automatically adjusts the instrument to get it exceptionally close without coming into contact with the rock.

“We have to make adjustments on the scale of micrometers to get the accuracy we need,” Allwood said. “It gets close enough to the rock to raise the hairs on the back of an engineer’s neck.”

Making a Mineral Map

Once PIXL is in position, another AI system gets the chance to shine. PIXL scans a postage-stamp-size area of a rock, firing an X-ray beam thousands of times to create a grid of microscopic dots. Each dot reveals information about the chemical composition of the minerals present.

Minerals are crucial to answering key questions about Mars. Depending on the rock, scientists might be on the hunt for carbonates, which hide clues to how water may have formed the rock, or they may be looking for phosphates, which could have provided nutrients for microbes, if any were present in the Martian past.

There’s no way for scientists to know ahead of time which of the hundreds of X-ray zaps will turn up a particular mineral, but when the instrument finds certain minerals, it can automatically stop to gather more data — an action called a “long dwell.” As the system improves through machine learning, the list of minerals on which PIXL can focus with a long dwell is growing.

“PIXL is kind of a Swiss army knife in that it can be configured depending on what the scientists are looking for at a given time,” said JPL’s David Thompson, who helped develop the software. “Mars is a great place to test out AI since we have regular communications each day, giving us a chance to make tweaks along the way.”

When future missions travel deeper into the solar system, they’ll be out of contact longer than missions currently are on Mars. That’s why there is strong interest in developing more autonomy for missions as they rove and conduct science for the benefit of humanity.

More About the Mission

A key objective for Perseverance’s mission on Mars is astrobiology, including the search for signs of ancient microbial life. The rover will characterize the planet’s geology and past climate, pave the way for human exploration of the Red Planet, and be the first mission to collect and cache Martian rock and regolith (broken rock and dust).

Subsequent NASA missions, in cooperation with ESA (European Space Agency), would send spacecraft to Mars to collect these sealed samples from the surface and return them to Earth for in-depth analysis.

The Mars 2020 Perseverance mission is part of NASA’s Moon to Mars exploration approach, which includes Artemis missions to the Moon that will help prepare for human exploration of the Red Planet.

JPL, which is managed for NASA by Caltech in Pasadena, California, built and manages operations of the Perseverance rover.

For more about Perseverance:

mars.nasa.gov/mars2020/

News Media Contacts

Andrew Good
Jet Propulsion Laboratory, Pasadena, Calif.
818-393-2433
andrew.c.good@jpl.nasa.gov

Karen Fox / Alana Johnson
NASA Headquarters, Washington
202-358-1600 / 202-358-1501
karen.c.fox@nasa.gov / alana.r.johnson@nasa.gov

2024-099

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      CSESP opportunities focus on developing and implementing projects that harness contributions from members of the general public to advance our understanding of Earth as a system. Proposals for the 2024 request were required to demonstrate a clear link between citizen science and NASA observation systems to advance the agency’s Earth science mission. Nine projects received funding.
      Engaging Citizen Scientists for Inclusive Earth Systems Monitoring, PI Duan Biggs, Northern Arizona University. Measure trees in tropical regions south of the equator with the GLOBE Observer App to improve models of vegetation structure and biomass models from NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission. Integrating Remote Sensing and Citizen Science to Support Conservation of Woodland Vernal Pools, PI Laura Bourgeau-Chavez, Michigan Technological University. Map and monitor shallow, seasonal wetlands in Michigan, Wisconsin and New York to better understand these key habitats of amphibians and other invertebrates. Citizen-Enabled Measurement of PM2.5 and Black Carbon: Addressing Local Inequities and Validating PM Composition from MAIA, Albert Presto/Carnegie Mellon University. Deploy sensors to measure sources of fine airborne particle pollution filling gaps in data from NASA’s Multi-Angle Imager for Aerosols (MAIA) mission. Expanding Citizen Science Hail Observations for Validation of NASA Satellite Algorithms and Understanding of Hail Melt, PI Russ Schumacher, Colorado State University. Measure the sizes and shapes of hailstones, starting in the southeastern United States, using photographs and special pads to help us understand microwave satellite data.  X-Snow: A Citizen-Science Proposal for Snow in the New York Area, PI, Marco Tedesco, Columbia University. Measure snow in the Catskill and Adirondacks regions of New York to help improve NASA’s models of snow depth and water content. Coupling Citizen Science and Remote Sensing Observations to Assess the Impacts of Icebergs on Coastal Arctic Ecosystems, PI, Maria Vernet, University of California, San Diego. Measure phytoplankton samples in polar regions to understand how icebergs and their meltwater affect phytoplankton concentration and biodiversity.  Forecasting Mosquito-Borne Disease Risk in a Changing Climate: Integrating GLOBE Citizen Science and NASA Earth System Modeling, PI Di Yang, University of Florida, Gainesville. Using data on mosquitoes from the GLOBE Observer App to predict future changes in mosquito-borne disease risk. Ozone Measurements from General Aviation: Supporting TEMPO Satellite Validation and Addressing Air Quality Issues in California’s San Joaquin Valley with Citizen Science, PI Emma Yates, NASA Ames Research Center. Deploy air-quality sensors around Bakersfield, California and compare the data to measurements from NASA’s Tropospheric Emissions Monitoring of Pollution instrument (TEMPO). Under the Canopy: Capturing the Role of Understory Phenology on Animal Communities Using Citizen Science, PI Benjamin Zuckerberg, University of Wisconsin, Madison. Measure snow depth, temperature, and sound in forest understories to improve satellite-based models of vegetation and snow cover for better modeling of wildlife communities.  For more information on citizen science awards from previous years, see articles from: 
      September 2023  August 2022 July 2021 For more information on NASA’s citizen science programs, visit https://science.nasa.gov/citizenscience.
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