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By NASA
Illustration of the main asteroid belt, orbiting the Sun between Mars and JupiterNASA NASA’s powerful James Webb Space Telescope includes asteroids on its list of objects studied and secrets revealed.
A team led by researchers at the Massachusetts Institute of Technology (MIT) in Cambridge repurposed Webb’s observations of a distant star to reveal a population of small asteroids — smaller than astronomers had ever detected orbiting the Sun in the main asteroid belt between Mars and Jupiter.
The 138 new asteroids range from the size of a bus to the size of a stadium — a size range in the main belt that has not been observable with ground-based telescopes. Knowing how many main belt asteroids are in different size ranges can tell us something about how asteroids have been changed over time by collisions. That process is related to how some of them have escaped the main belt over the solar system’s history, and even how meteorites end up on Earth.
“We now understand more about how small objects in the asteroid belt are formed and how many there could be,” said Tom Greene, an astrophysicist at NASA’s Ames Research Center in California’s Silicon Valley and co-author on the paper presenting the results. “Asteroids this size likely formed from collisions between larger ones in the main belt and are likely to drift towards the vicinity of Earth and the Sun.”
Insights from this research could inform the work of the Asteroid Threat Assessment Project at Ames. ATAP works across disciplines to support NASA’s Planetary Defense Coordination Office by studying what would happen in the case of an Earth impact and modeling the associated risks.
“It’s exciting that Webb’s capabilities can be used to glean insights into asteroids,” said Jessie Dotson, an astrophysicist at Ames and member of ATAP. “Understanding the sizes, numbers, and evolutionary history of smaller main belt asteroids provides important background about the near-Earth asteroids we study for planetary defense.”
Illustration of the James Webb Space TelescopeNASA The team that made the asteroid detections, led by research scientist Artem Burdanov and professor of planetary science Julien de Wit, both of MIT, developed a method to analyze existing Webb images for the presence of asteroids that may have been inadvertently “caught on film” as they passed in front of the telescope. Using the new image processing technique, they studied more than 10,000 images of the star TRAPPIST-1, originally taken to search for atmospheres around planets orbiting the star, in the search for life beyond Earth.
Asteroids shine more brightly in infrared light, the wavelength Webb is tuned to detect, than in visible light, helping reveal the population of main belt asteroids that had gone unnoticed until now. NASA will also take advantage of that infrared glow with an upcoming mission, the Near-Earth Object (NEO) Surveyor. NEO Surveyor is the first space telescope specifically designed to hunt for near-Earth asteroids and comets that may be potential hazards to Earth.
The paper presenting this research, “Detections of decameter main-belt asteroids with JWST,” was published Dec. 9 in Nature.
The James Webb Space Telescope is the world’s premier space science observatory. Webb is solving mysteries in our solar system, looking beyond to distant worlds around other stars, and probing the mysterious structures and origins of our universe and our place in it. Webb is an international program led by NASA with its partners, ESA (European Space Agency) and CSA (Canadian Space Agency).
For news media:
Members of the news media interested in covering this topic should reach out to the NASA Ames newsroom.
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By USH
The ongoing mystery and debate surrounding UFO and drone sightings across the U.S. continue to captivate public attention. The lack of transparency and definitive answers from government agencies combined with the apparent absence of military action against these drones, has fueled speculation about possible cover-ups or incompetence.
Local, county, and state governments seem to have no knowledge of who is operating these drones, where they originate, or their purpose. Despite this, officials confidently assert that "there is no credible threat." This raises the question: how can they be so certain? The reality suggests they cannot.
Recently, the Pentagon issued a statement following claims by a New Jersey congressman that Iran had deployed a "mothership" off the U.S. East Coast, launching drones. The Pentagon denied any military origin for the drones and ruled out links to known foreign entities, but questions persist about whether critical information is being withheld.
If these drones are not linked to Iran, the U.S., Russia, China, or any other nation, some experts propose they may be part of clandestine "deep state" programs. These programs could involve advanced aerospace technologies being tested by private companies under classified initiatives.
Witness accounts, including those from a New Jersey sheriff and Coast Guard officials, suggest the drones exhibit highly unusual behaviors. These include emerging from the ocean and performing movements like abrupt 90-degree turns—characteristics that could imply the use of advanced propulsion systems not publicly known.
Another theory posits that the drones may not be physical objects at all but rather holographic projections, akin to the controversial "Project Blue Beam" concept. If true, this would explain why attempts to intercept them could fail—they might not physically exist.
The sheer number, endurance, and sophistication of these drones hint at a coordinated operation. Some theorists believe this might be part of a psychological operation designed to distract from pressing political, economic, or social issues. The timing of such events often appears suspiciously aligned with periods of public, economic unrest or uncertainty.
In the event that the "deep state" is orchestrating these phenomena, some fear it could be a prelude to a false flag operation, with motives and consequences yet to be revealed.
The situation remains shrouded in speculation, leaving the public to grapple with more questions than answers.
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By NASA
Webb Webb News Latest News Latest Images Blog (offsite) Awards X (offsite – login reqd) Instagram (offsite – login reqd) Facebook (offsite- login reqd) Youtube (offsite) Overview About Who is James Webb? Fact Sheet Impacts+Benefits FAQ Science Overview and Goals Early Universe Galaxies Over Time Star Lifecycle Other Worlds Observatory Overview Launch Orbit Mirrors Sunshield Instrument: NIRCam Instrument: MIRI Instrument: NIRSpec Instrument: FGS/NIRISS Optical Telescope Element Backplane Spacecraft Bus Instrument Module Multimedia About Webb Images Images Videos What is Webb Observing? 3d Webb in 3d Solar System Podcasts Webb Image Sonifications Team International Team People Of Webb More For the Media For Scientists For Educators For Fun/Learning 6 Min Read Found: First Actively Forming Galaxy as Lightweight as Young Milky Way
Hundreds of overlapping objects at various distances are spread across this field. At the very center is a tiny galaxy nicknamed Firefly Sparkle that looks like a long, angled, dotted line. Smaller companions are nearby. Credits:
NASA, ESA, CSA, STScI, Chris Willott (National Research Council Canada), Lamiya Mowla (Wellesley College), Kartheik Iyer (Columbia University) For the first time, NASA’s James Webb Space Telescope has detected and “weighed” a galaxy that not only existed around 600 million years after the big bang, but is also similar to what our Milky Way galaxy’s mass might have been at the same stage of development. Other galaxies Webb has detected at this time period are significantly more massive. Nicknamed the Firefly Sparkle, this galaxy is gleaming with star clusters — 10 in all — each of which researchers examined in great detail.
Image A: Firefly Sparkle Galaxy and Companions in Galaxy Cluster MACS J1423 (NIRCam Image)
For the first time, astronomers using NASA’s James Webb Space Telescope have identified a galaxy, nicknamed the Firefly Sparkle, that not only is in the process of assembling and forming stars around 600 million years after the big bang, but also weighs about the same as our Milky Way galaxy if we could “wind back the clock” to weigh it as it developed. Two companion galaxies are close by, which may ultimately affect how this galaxy forms and builds mass over billions of years. NASA, ESA, CSA, STScI, Chris Willott (National Research Council Canada), Lamiya Mowla (Wellesley College), Kartheik Iyer (Columbia University) “I didn’t think it would be possible to resolve a galaxy that existed so early in the universe into so many distinct components, let alone find that its mass is similar to our own galaxy’s when it was in the process of forming,” said Lamiya Mowla, co-lead author of the paper and an assistant professor at Wellesley College in Massachusetts. “There is so much going on inside this tiny galaxy, including so many different phases of star formation.”
Webb was able to image the galaxy in crisp detail for two reasons. One is a benefit of the cosmos: A massive foreground galaxy cluster radically enhanced the distant galaxy’s appearance through a natural effect known as gravitational lensing. And when combined with the telescope’s specialization in high-resolution infrared light, Webb delivered unprecedented new data about the galaxy’s contents.
Image B: Galaxy Cluster MACS J1423 (NIRCam Image)
In this image from NASA’s James Webb Space Telescope, thousands of glimmering galaxies are bound together by their own gravity, making up a massive cluster formally classified as MACS J1423. The largest, bright white oval is a supergiant elliptical galaxy. The galaxy cluster acts like a lens, magnifying and distorting the light of objects that lie well behind it, an effect known as gravitational lensing. NASA, ESA, CSA, STScI, Chris Willott (National Research Council Canada), Lamiya Mowla (Wellesley College), Kartheik Iyer (Columbia University) “Without the benefit of this gravitational lens, we would not be able to resolve this galaxy,” said Kartheik Iyer, co-lead author and NASA Hubble Fellow at Columbia University in New York. “We knew to expect it based on current physics, but it’s surprising that we actually saw it.”
Mowla, who spotted the galaxy in Webb’s image, was drawn to its gleaming star clusters, because objects that sparkle typically indicate they are extremely clumpy and complicated. Since the galaxy looks like a “sparkle” or swarm of lightning bugs on a warm summer night, they named it the Firefly Sparkle galaxy.
Reconstructing the Galaxy’s Appearance
The research team modeled what the galaxy might have looked like if it weren’t stretched and discovered that it resembled an elongated raindrop. Suspended within it are two star clusters toward the top and eight toward the bottom. “Our reconstruction shows that clumps of actively forming stars are surrounded by diffuse light from other unresolved stars,” said Iyer. “This galaxy is literally in the process of assembling.”
Webb’s data shows the Firefly Sparkle galaxy is on the smaller side, falling into the category of a low-mass galaxy. Billions of years will pass before it builds its full heft and a distinct shape. “Most of the other galaxies Webb has shown us aren’t magnified or stretched, and we are not able to see their ‘building blocks’ separately. With Firefly Sparkle, we are witnessing a galaxy being assembled brick by brick,” Mowla said.
Stretched Out and Shining, Ready for Close Analysis
Since the galaxy is warped into a long arc, the researchers easily picked out 10 distinct star clusters, which are emitting the bulk of the galaxy’s light. They are represented here in shades of pink, purple, and blue. Those colors in Webb’s images and its supporting spectra confirmed that star formation didn’t happen all at once in this galaxy, but was staggered in time.
“This galaxy has a diverse population of star clusters, and it is remarkable that we can see them separately at such an early age of the universe,” said Chris Willott from the National Research Council of Canada’s Herzberg Astronomy and Astrophysics Research Centre, a co-author and the observation program’s principal investigator. “Each clump of stars is undergoing a different phase of formation or evolution.”
The galaxy’s projected shape shows that its stars haven’t settled into a central bulge or a thin, flattened disk, another piece of evidence that the galaxy is still forming.
Image C: Illustration of the Firefly Sparkle Galaxy in the Early Universe (Artist’s Concept)
This artist concept depicts a reconstruction of what the Firefly Sparkle galaxy looked like about 600 million years after the big bang if it wasn’t stretched and distorted by a natural effect known as gravitational lensing. This illustration is based on images and data from NASA’s James Webb Space Telescope. Illustration: NASA, ESA, CSA, Ralf Crawford (STScI). Science: Lamiya Mowla (Wellesley College), Guillaume Desprez (Saint Mary’s University) Video: “Firefly Sparkle” Reveals Early Galaxy
‘Glowing’ Companions
Researchers can’t predict how this disorganized galaxy will build up and take shape over billions of years, but there are two galaxies that the team confirmed are “hanging out” within a tight perimeter and may influence how it builds mass over billions of years.
Firefly Sparkle is only 6,500 light-years away from its first companion, and its second companion is separated by 42,000 light-years. For context, the fully formed Milky Way is about 100,000 light-years across — all three would fit inside it. Not only are its companions very close, the researchers also think that they are orbiting one another.
Each time one galaxy passes another, gas condenses and cools, allowing new stars to form in clumps, adding to the galaxies’ masses. “It has long been predicted that galaxies in the early universe form through successive interactions and mergers with other tinier galaxies,” said Yoshihisa Asada, a co-author and doctoral student at Kyoto University in Japan. “We might be witnessing this process in action.”
The team’s research relied on data from Webb’s CAnadian NIRISS Unbiased Cluster Survey (CANUCS), which includes near-infrared images from NIRCam (Near-Infrared Camera) and spectra from the microshutter array aboard NIRSpec (Near-Infrared Spectrograph). The CANUCS data intentionally covered a field that NASA’s Hubble Space Telescope imaged as part of its Cluster Lensing And Supernova survey with Hubble (CLASH) program.
This work has been published on December 11, 2024 in the journal Nature.
The James Webb Space Telescope is the world’s premier space science observatory. Webb is solving mysteries in our solar system, looking beyond to distant worlds around other stars, and probing the mysterious structures and origins of our universe and our place in it. Webb is an international program led by NASA with its partners, ESA (European Space Agency) and CSA (Canadian Space Agency).
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Media Contacts
Laura Betz – laura.e.betz@nasa.gov
NASA’s Goddard Space Flight Center, Greenbelt, Md.
Claire Blome – cblome@stsci.edu, Christine Pulliam – cpulliam@stsci.edu
Space Telescope Science Institute, Baltimore, Md.
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Last Updated Dec 10, 2024 Editor Marty McCoy Contact Laura Betz laura.e.betz@nasa.gov Related Terms
Astrophysics Galaxies Galaxy clusters Goddard Space Flight Center Gravitational Lensing James Webb Space Telescope (JWST) Science & Research The Universe View the full article
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By NASA
6 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
This animation shows data taken by NASA’s PACE and the international SWOT satellites over a region of the North Atlantic Ocean. PACE captured phytoplankton data on Aug. 8, 2024; layered on top is SWOT sea level data taken on Aug. 7 and 8, 2024. NASA’s Scientific Visualization Studio One Earth satellite can see plankton that photosynthesize. The other measures water surface height. Together, their data reveals how sea life and the ocean are intertwined.
The ocean is an engine that drives Earth’s weather patterns and climate and sustains a substantial portion of life on the planet. A new animation based on data from two recently launched missions — NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) and the international Surface Water and Ocean Topography (SWOT) satellites — gives a peek into the heart of that engine.
Physical processes, including localized swirling water masses called eddies and the vertical movement of water, can drive nutrient availability in the ocean. In turn, those nutrients determine the location and concentration of tiny floating organisms known as phytoplankton that photosynthesize, converting sunlight into food. These organisms have not only contributed roughly half of Earth’s oxygen since the planet formed, but also support economically important fisheries and help draw carbon out of the atmosphere, locking it away in the deep sea.
“We see great opportunity to dramatically accelerate our scientific understanding of our oceans and the significant role they play in our Earth system,” said Karen St. Germain, director of the Earth Science Division at NASA Headquarters in Washington. “This visualization illustrates the potential we have when we begin to integrate measurements from our separate SWOT and PACE ocean missions. Each of those missions is significant on its own. But bringing their data together — the physics from SWOT and the biology from PACE — gives us an even better view of what’s happening in our oceans, how they are changing, and why.”
A collaboration between NASA and the French space agency CNES (Centre National d’Études Spatiales), the SWOT’ satellite launched in December 2022 to measure the height of nearly all water on Earth’s surface. It is providing one of the most detailed, comprehensive views yet of the planet’s ocean and its freshwater lakes, reservoirs, and rivers.
Launched in February 2024, NASA’s PACE satellite detects and measures the distribution of phytoplankton communities in the ocean. It also provides data on the size, amount, and type of tiny particles called aerosols in Earth’s atmosphere, as well as the height, thickness, and opacity of clouds.
“Integrating information across NASA’s Earth System Observatory and its pathfinder missions SWOT and PACE is an exciting new frontier in Earth science,” said Nadya Vinogradova Shiffer, program scientist for SWOT and the Integrated Earth System Observatory at NASA Headquarters.
Where Physics and Biology Meet
The animation above starts by depicting the orbits of SWOT (orange) and PACE (light blue), then zooms into the North Atlantic Ocean. The first data to appear was acquired by PACE on Aug. 8. It reveals concentrations of chlorophyll-a, a vital pigment for photosynthesis in plants and phytoplankton. Light green and yellow indicate higher concentrations of chlorophyll-a, while blue signals lower concentrations.
Next is sea surface height data from SWOT, taken during several passes over the same region between Aug. 7 and 8. Dark blue represents heights that are lower than the mean sea surface height, while dark orange and red represent heights higher than the mean. The contour lines that remain once the color fades from the SWOT data indicate areas of the ocean with the same height, much like the lines on a topographic map indicate areas with the same elevation.
The underlying PACE data then cycles through several groups of phytoplankton, starting with picoeukaryotes. Lighter green indicates greater concentrations of this group. The final two groups are cyanobacteria — some of the smallest and most abundant phytoplankton in the ocean — called Prochlorococcus and Synechococcus. For Prochlorococcus, lighter raspberry colors represent higher concentrations. Lighter teal colors for Synechococcus signal greater amounts of the cyanobacteria.
The animation shows that higher phytoplankton concentrations on Aug. 8 tended to coincide with areas of lower water height. Eddies that spin counterclockwise in the Northern Hemisphere tend to draw water away from their center. This results in relatively lower sea surface heights in the center that draw up cooler, nutrient-rich water from the deep ocean. These nutrients act like fertilizer, which can boost phytoplankton growth in sunlit waters at the surface.
Overlapping SWOT and PACE data enables a better understanding of the connections between ocean dynamics and aquatic ecosystems, which can help improve the management of resources such as fisheries, since phytoplankton form the base of most food chains in the sea. Integrating these kinds of datasets also helps to improve calculations of how much carbon is exchanged between the atmosphere and the ocean. This, in turn, can indicate whether regions of the ocean that absorb excess atmospheric carbon are changing.
More About SWOT
The SWOT satellite was jointly developed by NASA and CNES, with contributions from the Canadian Space Agency (CSA) and the UK Space Agency. NASA’s Jet Propulsion Laboratory, managed for the agency by Caltech in Pasadena, California, leads the U.S. component of the project. For the flight system payload, NASA provided the Ka-band radar interferometer (KaRIn) instrument, a GPS science receiver, a laser retroreflector, a two-beam microwave radiometer, and NASA instrument operations. The Doppler Orbitography and Radioposition Integrated by Satellite system, the dual frequency Poseidon altimeter (developed by Thales Alenia Space), the KaRIn radio-frequency subsystem (together with Thales Alenia Space and with support from the UK Space Agency), the satellite platform, and ground operations were provided by CNES. The KaRIn high-power transmitter assembly was provided by CSA.
To learn more about SWOT, visit:
https://swot.jpl.nasa.gov
More About PACE
The PACE mission is managed by NASA Goddard Space Flight Center, which also built and tested the spacecraft and the Ocean Color Instrument, which collected the data shown in the visualization. The satellite’s Hyper-Angular Rainbow Polarimeter #2 was designed and built by the University of Maryland, Baltimore County, and the Spectro-polarimeter for Planetary Exploration was developed and built by a Dutch consortium led by Netherlands Institute for Space Research, Airbus Defence, and Space Netherlands.
To learn more about PACE, visit:
https://pace.gsfc.nasa.gov
News Media Contacts
Jacob Richmond (for PACE)
NASA’s Goddard Space Flight Center, Greenbelt, Md.
jacob.a.richmond@nasa.gov
Jane J. Lee / Andrew Wang (for SWOT)
Jet Propulsion Laboratory, Pasadena, Calif.
818-354-0307 / 626-379-6874
jane.j.lee@jpl.nasa.gov / andrew.wang@jpl.nasa.gov
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Last Updated Dec 09, 2024 Related Terms
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By NASA
9 Min Read Towards Autonomous Surface Missions on Ocean Worlds
Artist’s concept image of a spacecraft lander with a robot arm on the surface of Europa. Credits:
NASA/JPL – Caltech Through advanced autonomy testbed programs, NASA is setting the groundwork for one of its top priorities—the search for signs of life and potentially habitable bodies in our solar system and beyond. The prime destinations for such exploration are bodies containing liquid water, such as Jupiter’s moon Europa and Saturn’s moon Enceladus. Initial missions to the surfaces of these “ocean worlds” will be robotic and require a high degree of onboard autonomy due to long Earth-communication lags and blackouts, harsh surface environments, and limited battery life.
Technologies that can enable spacecraft autonomy generally fall under the umbrella of Artificial Intelligence (AI) and have been evolving rapidly in recent years. Many such technologies, including machine learning, causal reasoning, and generative AI, are being advanced at non-NASA institutions.
NASA started a program in 2018 to take advantage of these advancements to enable future icy world missions. It sponsored the development of the physical Ocean Worlds Lander Autonomy Testbed (OWLAT) at NASA’s Jet Propulsion Laboratory in Southern California and the virtual Ocean Worlds Autonomy Testbed for Exploration, Research, and Simulation (OceanWATERS) at NASA’s Ames Research Center in Silicon Valley, California.
NASA solicited applications for its Autonomous Robotics Research for Ocean Worlds (ARROW) program in 2020, and for the Concepts for Ocean worlds Life Detection Technology (COLDTech) program in 2021. Six research teams, based at universities and companies throughout the United States, were chosen to develop and demonstrate autonomy solutions on OWLAT and OceanWATERS. These two- to three-year projects are now complete and have addressed a wide variety of autonomy challenges faced by potential ocean world surface missions.
OWLAT
OWLAT is designed to simulate a spacecraft lander with a robotic arm for science operations on an ocean world body. The overall OWLAT architecture including hardware and software components is shown in Figure 1. Each of the OWLAT components is detailed below.
Figure 1. The software and hardware components of the Ocean Worlds Lander Autonomy Testbed and the relationships between them. NASA/JPL – Caltech The hardware version of OWLAT (shown in Figure 2) is designed to physically simulate motions of a lander as operations are performed in a low-gravity environment using a six degrees-of-freedom (DOF) Stewart platform. A seven DOF robot arm is mounted on the lander to perform sampling and other science operations that interact with the environment. A camera mounted on a pan-and-tilt unit is used for perception. The testbed also has a suite of onboard force/torque sensors to measure motion and reaction forces as the lander interacts with the environment. Control algorithms implemented on the testbed enable it to exhibit dynamics behavior as if it were a lightweight arm on a lander operating in different gravitational environments.
Figure 2. The Ocean Worlds Lander Autonomy Testbed. A scoop is mounted to the end of the testbed robot arm. NASA/JPL – Caltech The team also developed a set of tools and instruments (shown in Figure 3) to enable the performance of science operations using the testbed. These various tools can be mounted to the end of the robot arm via a quick-connect-disconnect mechanism. The testbed workspace where sampling and other science operations are conducted incorporates an environment designed to represent the scene and surface simulant material potentially found on ocean worlds.
Figure 3. Tools and instruments designed to be used with the testbed. NASA/JPL – Caltech The software-only version of OWLAT models, visualizes, and provides telemetry from a high-fidelity dynamics simulator based on the Dynamics And Real-Time Simulation (DARTS) physics engine developed at JPL. It replicates the behavior of the physical testbed in response to commands and provides telemetry to the autonomy software. A visualization from the simulator is shown on Figure 4.
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Figure 7. Screenshot of OceanWATERS lander on a terrain modeled from the Atacama Desert. A scoop operation has just been completed. NASA/JPL – Caltech The autonomy software module shown at the top in Figure 1 interacts with the testbed through a Robot Operating System (ROS)-based interface to issue commands and receive telemetry. This interface is defined to be identical to the OceanWATERS interface. Commands received from the autonomy module are processed through the dispatcher/scheduler/controller module (blue box in Figure 1) and used to command either the physical hardware version of the testbed or the dynamics simulation (software version) of the testbed. Sensor information from the operation of either the software-only or physical testbed is reported back to the autonomy module using a defined telemetry interface. A safety and performance monitoring and evaluation software module (red box in Figure 1) ensures that the testbed is kept within its operating bounds. Any commands causing out of bounds behavior and anomalies are reported as faults to the autonomy software module.
Figure 5. Erica Tevere (at the operator’s station) and Ashish Goel (at the robot arm) setting up the OWLAT testbed for use. NASA/JPL – Caltech OceanWATERS
At the time of the OceanWATERS project’s inception, Jupiter’s moon Europa was planetary science’s first choice in searching for life. Based on ROS, OceanWATERS is a software tool that provides a visual and physical simulation of a robotic lander on the surface of Europa (see Figure 6). OceanWATERS realistically simulates Europa’s celestial sphere and sunlight, both direct and indirect. Because we don’t yet have detailed information about the surface of Europa, users can select from terrain models with a variety of surface and material properties. One of these models is a digital replication of a portion of the Atacama Desert in Chile, an area considered a potential Earth-analog for some extraterrestrial surfaces.
Figure 6. Screenshot of OceanWATERS. NASA/JPL – Caltech JPL’s Europa Lander Study of 2016, a guiding document for the development of OceanWATERS, describes a planetary lander whose purpose is collecting subsurface regolith/ice samples, analyzing them with onboard science instruments, and transmitting results of the analysis to Earth.
The simulated lander in OceanWATERS has an antenna mast that pans and tilts; attached to it are stereo cameras and spotlights. It has a 6 degree-of-freedom arm with two interchangeable end effectors—a grinder designed for digging trenches, and a scoop for collecting ground material. The lander is powered by a simulated non-rechargeable battery pack. Power consumption, the battery’s state, and its remaining life are regularly predicted with the Generic Software Architecture for Prognostics (GSAP) tool. To simulate degraded or broken subsystems, a variety of faults (e.g., a frozen arm joint or overheating battery) can be “injected” into the simulation by the user; some faults can also occur “naturally” as the simulation progresses, e.g., if components become over-stressed. All the operations and telemetry (data measurements) of the lander are accessible via an interface that external autonomy software modules can use to command the lander and understand its state. (OceanWATERS and OWLAT share a unified autonomy interface based on ROS.) The OceanWATERS package includes one basic autonomy module, a facility for executing plans (autonomy specifications) written in the PLan EXecution Interchange Language, or PLEXIL. PLEXIL and GSAP are both open-source software packages developed at Ames and available on GitHub, as is OceanWATERS.
Mission operations that can be simulated by OceanWATERS include visually surveying the landing site, poking at the ground to determine its hardness, digging a trench, and scooping ground material that can be discarded or deposited in a sample collection bin. Communication with Earth, sample analysis, and other operations of a real lander mission, are not presently modeled in OceanWATERS except for their estimated power consumption. Figure 7 is a video of OceanWATERS running a sample mission scenario using the Atacama-based terrain model.
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Figure 7. Screenshot of OceanWATERS lander on a terrain modeled from the Atacama Desert. A scoop operation has just been completed. NASA/JPL – Caltech Because of Earth’s distance from the ocean worlds and the resulting communication lag, a planetary lander should be programmed with at least enough information to begin its mission. But there will be situation-specific challenges that will require onboard intelligence, such as deciding exactly where and how to collect samples, dealing with unexpected issues and hardware faults, and prioritizing operations based on remaining power.
Results
All six of the research teams funded by the ARROW and COLDTech programs used OceanWATERS to develop ocean world lander autonomy technology and three of those teams also used OWLAT. The products of these efforts were published in technical papers, and resulted in development of software that may be used or adapted for actual ocean world lander missions in the future. The following table summarizes the ARROW and COLDTech efforts.
Principal Investigator (PI) PI Institution Project Testbed Used Purpose of Project ARROW Projects Jonathan Bohren Honeybee Robotics Stochastic PLEXIL (SPLEXIL) OceanWATERS Extended PLEXIL with stochastic decision-making capabilities by employing reinforcement learning techniques. Pooyan Jamshidi University of South Carolina Resource Adaptive Software Purpose-Built for Extraordinary Robotic Research Yields (RASPBERRY SI) OceanWATERS & OWLAT Developed software algorithms and tools for fault root cause identification, causal debugging, causal optimization, and causal-induced verification. COLDTech Projects Eric Dixon Lockheed Martin Causal And Reinforcement Learning (CARL) for COLDTech OceanWATERS Integrated a model of JPL’s mission-ready Cold Operable Lunar Deployable Arm (COLDarm) into OceanWATERS and applied image analysis, causal reasoning, and machine learning models to identify and mitigate the root causes of faults, such as ice buildup on the arm’s end effector. Jay McMahon University of Colorado Robust Exploration with Autonomous Science On-board, Ranked Evaluation of Contingent Opportunities for Uninterrupted Remote Science Exploration (REASON-RECOURSE) OceanWATERS Applied automated planning with formal methods to maximize science return of the lander while minimizing communication with ground team on Earth. Melkior Ornik U Illinois, Urbana-Champaign aDaptive, ResIlient Learning-enabLed oceAn World AutonomY (DRILLAWAY) OceanWATERS & OWLAT Developed autonomous adaptation to novel terrains and selecting scooping actions based on the available image data and limited experience by transferring the scooping procedure learned from a low-fidelity testbed to the high-fidelity OWLAT testbed. Joel Burdick Caltech Robust, Explainable Autonomy for Scientific Icy Moon Operations (REASIMO) OceanWATERS & OWLAT Developed autonomous 1) detection and identification of off-nominal conditions and procedures for recovery from those conditions, and 2) sample site selection Acknowledgements: The portion of the research carried out at the Jet Propulsion Laboratory, California Institute of Technology was performed under a contract with the National Aeronautics and Space Administration (80NM0018D0004). The portion of the research carried out by employees of KBR Wyle Services LLC at NASA Ames Research Center was performed under a contract with the National Aeronautics and Space Administration (80ARC020D0010). Both were funded by the Planetary Science Division ARROW and COLDTech programs.
Project Leads: Hari Nayar (NASA Jet Propulsion Laboratory, California Institute of Technology), K. Michael Dalal (KBR, Inc. at NASA Ames Research Center)
Sponsoring Organizations: NASA SMD PESTO
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