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By NASA
On Jan. 7, 1610, Italian astronomer Galileo Galilei peered through his newly improved 20-power homemade telescope at the planet Jupiter. He noticed three other points of light near the planet, at first believing them to be distant stars. Observing them over several nights, he noted that they appeared to move in the wrong direction with regard to the background stars and they remained in Jupiter’s proximity but changed their positions relative to one another. Four days later, he observed a fourth point of light near the planet with the same unusual behavior. By Jan. 15, Galileo correctly concluded that he had discovered four moons orbiting around Jupiter, providing strong evidence for the Copernican theory that most celestial objects did not revolve around the Earth.
Two of Galileo’s telescopes.National Geographic. Painting by Giuseppe Bertini (1858) of Galileo demonstrating his telescope to the Doge of Venice.gabrielevanin.it Page from Galileo’s notebook about his observations of Jupiter’s satellites.University of Michigan Special Collections Library. In March 1610, Galileo published his discoveries of Jupiter’s satellites and other celestial observations in a book titled Siderius Nuncius (The Starry Messenger). As their discoverer, Galileo had naming rights to Jupiter’s satellites. He proposed to name them after his patrons the Medicis and astronomers called them the Medicean Stars through much of the seventeenth century, although in his own notes Galileo referred to them by the Roman numerals I, II, III, and IV, in order of their distance from Jupiter. Astronomers still refer to the four moons as the Galilean satellites in honor of their discoverer.
In 1614, the German astronomer Johannes Kepler suggested naming the satellites after mythological figures associated with Jupiter, namely Io, Europa, Ganymede, and Callisto, but his idea didn’t catch on for more than 200 years. Scientists didn’t discover any more satellites around Jupiter until 1892 when American astronomer E.E. Barnard found Jupiter’s fifth moon Amalthea, much smaller than the Galilean moons and orbiting closer to the planet than Io. It was the last satellite in the solar system found by visual observation – all subsequent discoveries occurred via photography or digital imaging. As of today, astronomers have identified 95 moons orbiting Jupiter.
Image of Jupiter and three of its four Galilean satellites through an amateur telescope, similar to what Galileo might have seen. Hubble Space Telescope image of Jupiter and three of its four Galilean satellites during a rare triple transit. Although each of the Galilean satellites has unique features, such as the volcanoes of Io, the heavily cratered surface of Callisto, and the magnetic field of Ganymede, scientists have focused more attention on Europa due to the tantalizing possibility that it might be hospitable to life. In the 1970s, NASA’s Pioneer 10 and 11 and Voyager 1 and 2 spacecraft took ever increasingly detailed images of the large satellites including Europa during their flybys of Jupiter. The photographs revealed Europa to have the smoothest surface of any object in the solar system, indicating a relatively young crust, and also one of the brightest of any satellite indicating a highly reflective surface. These features led scientists to hypothesize that Europa is covered by an icy crust floating on a subsurface salty ocean. They further postulated that tidal heating caused by Jupiter’s gravity reforms the surface ice layer in cycles of melting and freezing.
Image of Europa taken by Pioneer 10 during its flyby of Jupiter in 1973. Image of Europa taken by Voyager 1 during its 1979 flyby of Jupiter. Image of Europa taken by Voyager 2 during its 1979 flyby of Jupiter. More detailed observations from NASA’s Galileo spacecraft that orbited Jupiter between 1995 and 2003 and completed 11 close encounters with Europa revealed that long linear features on its surface may indicate tidal or tectonic activity. Reddish-brown material along the fissures and in splotches elsewhere on the surface may contain salts and sulfur compounds transported from below the crust and modified by radiation. Observations from the Hubble Space Telescope and re-analysis of images from Galileo revealed possible plumes emanating from beneath Europa’s crust, lending credence to that hypothesis. While the exact composition of this material is not known, it likely holds clues to whether Europa may be hospitable to life.
Global view of Europa from the Galileo spacecraft. More detailed views of varied terrain on Europa from Galileo. Cutaway illustration of Europa’s icy crust, subsurface ocean and possible vents that transport material to the surface. Future robotic explorers of Europa may answer some of the outstanding questions about this unique satellite of Jupiter. NASA’s Europa Clipper set off in October 2024 on a 5.5-year journey to Jupiter. After its arrival in 2030, the spacecraft will enter orbit around the giant planet and conduct 49 flybys of Europa during its four-year mission. Managed by the Jet Propulsion Laboratory in Pasadena, California, and the Applied Physics Laboratory at Johns Hopkins University in Baltimore, Maryland, Europa Clipper will carry nine instruments including imaging systems and a radar to better understand the structure of the icy crust. Data from Europa Clipper will complement information returned by the European Space Agency’s JUICE (Jupiter Icy Moon Explorer) spacecraft. Launched in April 2023, JUICE will first enter orbit around Jupiter in 2031 and then enter orbit around Ganymede in 2034. The spacecraft also plans to conduct studies of Europa complementary with Europa Clipper’s. The two spacecraft should greatly increase our understanding of Europa and perhaps uncover new mysteries.
Illustration of the Europa Clipper spacecraft investigating Europa. Illustration of the JUICE spacecraft exploring Europa.European Space Agency. View the full article
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By NASA
NASA Goddard MODIS Rapid Response Team During the morning of March 20, 2015, a total solar eclipse was visible from parts of Europe, and a partial solar eclipse from northern Africa and northern Asia. NASA’s Terra satellite passed over the Arctic Ocean on March 20 at 10:45 UTC (6:45 a.m. EDT) and captured the eclipse’s shadow over the clouds in the Arctic Ocean.
Terra launched 25 years ago on Dec. 18, 1999. Approximately the size of a small school bus, the Terra satellite carries five instruments that take coincident measurements of the Earth system: Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Clouds and Earth’s Radiant Energy System (CERES), Multi-angle Imaging Spectroradiometer (MISR), Measurements of Pollution in the Troposphere (MOPITT), and Moderate Resolution Imaging Spectroradiometer (MODIS).
On Nov. 28, 2024, one of Terra’s power-transmitting shunt units failed. A response team reviewed Terra’s status and discussed potential impacts and options. Consequently, the team placed ASTER into Safe Mode. As a result, ASTER data are not currently being collected. All other instruments continue uninterrupted.
Image Credit: NASA Goddard MODIS Rapid Response Team
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By NASA
4 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
The six SCALPSS cameras mounted around the base of Blue Ghost will collect imagery during and after descent and touchdown. Using a technique called stereo photogrammetry, researchers at Langley will use the overlapping images to produce a 3D view of the surface. Image courtesy of Firefly. Say cheese again, Moon. We’re coming in for another close-up.
For the second time in less than a year, a NASA technology designed to collect data on the interaction between a Moon lander’s rocket plume and the lunar surface is set to make the long journey to Earth’s nearest celestial neighbor for the benefit of humanity.
Developed at NASA’s Langley Research Center in Hampton, Virginia, Stereo Cameras for Lunar Plume-Surface Studies (SCALPSS) is an array of cameras placed around the base of a lunar lander to collect imagery during and after descent and touchdown. Using a technique called stereo photogrammetry, researchers at Langley will use the overlapping images from the version of SCALPSS on Firefly’s Blue Ghost — SCALPSS 1.1 — to produce a 3D view of the surface. An earlier version, SCALPSS 1.0, was on Intuitive Machines’ Odysseus spacecraft that landed on the Moon last February. Due to mission contingencies that arose during the landing, SCALPSS 1.0 was unable to collect imagery of the plume-surface interaction. The team was, however, able to operate the payload in transit and on the lunar surface following landing, which gives them confidence in the hardware for 1.1.
The SCALPSS 1.1 payload has two additional cameras — six total, compared to the four on SCALPSS 1.0 — and will begin taking images at a higher altitude, prior to the expected onset of plume-surface interaction, to provide a more accurate before-and-after comparison.
These images of the Moon’s surface won’t just be a technological novelty. As trips to the Moon increase and the number of payloads touching down in proximity to one another grows, scientists and engineers need to be able to accurately predict the effects of landings.
How much will the surface change? As a lander comes down, what happens to the lunar soil, or regolith, it ejects? With limited data collected during descent and landing to date, SCALPSS will be the first dedicated instrument to measure the effects of plume-surface interaction on the Moon in real time and help to answer these questions.
“If we’re placing things – landers, habitats, etc. – near each other, we could be sand blasting what’s next to us, so that’s going to drive requirements on protecting those other assets on the surface, which could add mass, and that mass ripples through the architecture,” said Michelle Munk, principal investigator for SCALPSS and acting chief architect for NASA’s Space Technology Mission Directorate at NASA Headquarters in Washington. “It’s all part of an integrated engineering problem.”
Under the Artemis campaign, the agency’s current lunar exploration approach, NASA is collaborating with commercial and international partners to establish the first long-term presence on the Moon. On this CLPS (Commercial Lunar Payload Services) initiative delivery carrying over 200 pounds of NASA science experiments and technology demonstrations, SCALPSS 1.1 will begin capturing imagery from before the time the lander’s plume begins interacting with the surface until after the landing is complete.
The final images will be gathered on a small onboard data storage unit before being sent to the lander for downlink back to Earth. The team will likely need at least a couple of months to
process the images, verify the data, and generate the 3D digital elevation maps of the surface. The expected lander-induced erosion they reveal probably won’t be very deep — not this time, anyway.
One of the SCALPSS cameras is visible here mounted to the Blue Ghost lander.Image courtesy of Firefly. “Even if you look at the old Apollo images — and the Apollo crewed landers were larger than these new robotic landers — you have to look really closely to see where the erosion took place,” said Rob Maddock, SCALPSS project manager at Langley. “We’re anticipating something on the order of centimeters deep — maybe an inch. It really depends on the landing site and how deep the regolith is and where the bedrock is.”
But this is a chance for researchers to see how well SCALPSS will work as the U.S. advances human landing systems as part of NASA’s plans to explore more of the lunar surface.
“Those are going to be much larger than even Apollo. Those are large engines, and they could conceivably dig some good-sized holes,” said Maddock. “So that’s what we’re doing. We’re collecting data we can use to validate the models that are predicting what will happen.”
The SCALPSS 1.1 project is funded by the Space Technology Mission Directorate’s Game Changing Development Program.
NASA is working with several American companies to deliver science and technology to the lunar surface under the CLPS initiative. Through this opportunity, various companies from a select group of vendors bid on delivering payloads for NASA including everything from payload integration and operations, to launching from Earth and landing on the surface of the Moon.
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Last Updated Dec 19, 2024 EditorAngelique HerringLocationNASA Langley Research Center 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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By NASA
2 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
ESI24 Zhai Quadchart
Lei Zhai
University of Central Florida
Lunar dust, with its chemical reactivity, electrostatic charge, and potential magnetism, poses a serious threat to astronauts and equipment on the Moon’s surface. To address this, the project proposes developing structured coatings with anisotropic surface features and electrostatic dissipative properties to passively mitigate lunar dust. By analyzing lunar dust-surface interactions at multiple scales, the team aims to optimize the coatings’ surface structures and physical properties, such as Young’s modulus, electrical conductivity, and polarity. The project will examine tribocharging, external electric fields, and the effects of particle shapes and sizes. Numerical sensitivity analyses will complement simulations to better understand lunar dust dynamics. Once fabricated, the coatings will be tested under simulated lunar conditions. The team will employ a state-of-the-art nanoscale force spectroscopy system, using atomic force microsope (AFM) microcantilevers functionalized with regolith to measure dust-surface interactions. Additional experiments will assess particle adhesion and removal, with scanning electron microscopy used to analyze remaining dust. This project aims to provide insights into surface structure effects on dust adhesion, guiding the creation of lightweight, durable coatings for effective dust mitigation. The findings will foster collaborations with NASA and the aerospace industry, while offering training opportunities for students entering the field.
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