Swarms of small satellites could communicate amongst themselves to collect data on important weather patterns at different times of the day or year, and from multiple angles. Such swarms, using machine learning algorithms, could revolutionize scientists’ understanding of weather and climate changes.
Engineer Sabrina Thompson is working on software to enable small spacecraft, or SmallSats, to communicate with each other, identify high-value observation targets, and coordinate attitude and timing to get different views of the same target.
“We already know that Saharan dust blowing over to the Amazon rainforests affects cloud formation over the Atlantic Ocean during certain times of the year,” said Thompson, who works at NASA’s Goddard Space Flight Center in Greenbelt, Maryland. “How do you capture that cloud formation? How do you tell a swarm of satellites what region and time of day is the best to observe that phenomenon?”
Under Thompson’s plan, scientists would establish a set of requirements for observations and define high-value targets. Then the software would take over, enabling a spacecraft swarm to figure out how to move relative to one another to best observe these targets. Strategies might also change based on time of day, season, or the region being observed. The spacecraft also would use onboard machine learning to improve viewing strategies over time.
Setting the following spacecraft to maximize drag and the leader to minimize drag will cause the follower to drop in altitude and catch up to the leader.Credits: NASA/Sabrina Thompson
“There are several types of swarm configuration being considered,” Thompson said. “One might be a swarm where satellites will be in different orbits, which will allow them to view a cloud or other phenomenon at different angles. Another swarm could view the same phenomena with similar view, but at different times of the day. A third type of swarm might combine both, with some satellites in the same orbit, following one another with some time offset, and other satellites which may be in orbits with different altitudes and/or inclinations.”
While a swarm would stay within the same orbit, individual spacecraft could even use something called differential drag control — manipulating the forces caused by Earth’s atmosphere dragging against the orbiting craft — to control the time separation between each spacecraft relative to others in the swarm, she said. “The length of time it takes to perform a differential drag maneuver depends on the spacecraft mass and area, as well as the orbital altitude. For instance, it can take as long as one year or as short as a couple of days, even hours.”
“With multiple spacecraft in one formation to view the same target,” Thompson said, “you can see a cloud, for instance, not just from the top, but from the sides as well.” In a different formation, you can see that cloud at different stages of its life-cycle from multiple SmallSats passing at different times.
A SmallSat like this one, working with a swarm of similar spacecraft with more narrow-angle, high-resolution polarimeters, could potentially revolutionize understanding of weather formation and processes.Credits: NASA/SDL/Jose Vanderlei Martins
Working with University of Maryland – Baltimore County (UMBC) professor Jose Vanderlei Martins, Thompson helped develop the Hyper-Angular Rainbow Polarimeter (HARP) CubeSat that launched from the International Space Station (ISS) just over a year ago. An updated version of its instrumentation, called HARP2, will fly on the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission planned for launch in 2023.
A swarm of SmallSats like HARP, sharing information and coordinating coverage, could advance weather forecasting, disaster reporting, and climate modeling in the long term, Vanderlei Martins said. To get there, scientists need the combination of wide and narrow fields of view and high-resolution imagery to better understand the dynamics of weather system development.
“Ideally, I like to have a satellite with a wide field of view observing larger phenomenon,” he said. “However, a small satellite covering a large area cannot make high spatial resolution observations. Nevertheless, you can use it as a surveyor type of satellite to identify the area of interest. Then you have others with a narrower field of view, getting higher resolution, getting much more detail.”
Enabling the swarm to make decisions and share information is crucial. Vanderlei Martins said, “These sorts of decisions need to be made in minutes. You don’t have time for ground control to be involved.”
Thompson noted that reducing reliance on ground control and communications networks also frees up resources for SmallSat missions with limited budgets.
As an aerospace engineer working towards an atmospheric physics degree at the University of Maryland, Baltimore County, Thompson went back to school to learn more about the Earth science requirements that drive her work as an innovator. “I also really wanted to understand climate change.”
How aerosol particles and clouds interact is crucial to understanding climate change. Polarimeters can provide a wealth of data about particles suspended in the atmosphere — from smoke, ash, and dust to water droplets and ice, each species of particle polarizes light reflected from it in detectable ways.
“At a basic level, my research involves evaluating the geometry between instruments on the satellite and the sun,” Thompson said. “These instruments are passive. They require a certain geometry relative to the ground target and Sun to retrieve the science data we want.”
Her algorithms will determine the most suitable combinations of orbit and instrument field of views to give the largest probability of observing a cloud with the appropriate geometry to retrieve science data. Then it would plan and execute maneuvering schemes for each spacecraft to achieve those geometries relative to the other satellites in the swarm.
This work to understand the structure and development of clouds ties in with the Atmosphere Observing System, or AOS, (formerly the Aerosols and Clouds, Convection and Precipitation study identified as a priority in the 2017 Earth Decadal Survey. Vanderlei Martins and Thompson believe their swarm technology complements AOS’s science objectives and could enhance upcoming NASA Earth science missions.
Banner Image: Two satellites on similar orbits collect valuable perspectives on the same part of the atmosphere. Credit: NASA/Sabrina Thompson
By Karl B. Hille
NASA’s Goddard Space Flight Center in Greenbelt, Md.Last Updated: Sep 2, 2021Editor: Karl Hille
Do they mean that they want a “Sky Net” as postulated in “The Terminator”? That had an innocent beginning too.
Well it IS a network and it’s in the sky, so…
Interesting, but you can’t change orbital altitude and/or speed by simply adjusting the drag of the satellite, eventually you need energy input. There are military satellites that have manuvering capability, but they consume the limited fuel onboard, so tasking is a complicated process. So a small satellite must dedicate part of the payload to fuel, which limits the amount of orbital changes, which is not what the “herd” coordination suggests. I’m doubtful about the effectiveness of this scheme.
They can just add a windmill for power.
They can use the Solar Wind. LOL
They are small, about 10 kgs each. You could load 100 onto a shuttle to the space station and disperse from there.
The answer could be to have so many satellites that you always have satellites where you need them. link
Waffle word pseudo promises…
Sounds like the kind of language used for budget request submissions. Glowing promises without hard achievements beyond putting satellites in space.
Machine learning, using existing parameters, could ensure temperatures are adjusted properly to produce the desired result without human intervention. Could save time and money and still encourage additional research funding.
Nice that they finally came right out and said it. “…enable small spacecraft, or SmallSats, to communicate with each other, identify high-value observation targets, and coordinate attitude and timing to get different views of the same target.”
My thoughts also.
The task you describe does not need nor benefit from machine learning.
That task is bog standard programming that requires far less code, less CPU, less storage and space to run.
Something most satellites already include in their programming, no human intervention required.
They are frantically trying to find a good application for the over-promised polarimeters.
What they need to concentrate on is satellite defenses so Putin and Winnie the Xiden can not destroy them.
“Swarms of small satellites could communicate amongst themselves to collect data on important weather patterns at different times of the day or year, “
Surely that’s
Swarms of small satellites could communicate amongst themselves to avoid collisions with other swarms of small satellites…?
The hackers will have a field day with this. 😉
Don’t kid yourself, the primary purpose is military snooping…just like Argo floaters.
The paranoia is strong in this one.
I thought of Derek and Clive when I saw the word ‘floaters’
Fun fact: with ever more satellites they are killing the low earth orbit. Just recently the ISS was hit by a piece of debris, earlier this year a chinese spy satellite was unintendedly taken out by some russian piece of space trash, producing more debris.
The “Kessler Syndrome” is making significant progress and is going to feed on thousands of new starlink satellites. It might soon render space inaccessible. Seems like the concept of sustainability has low priority with space travel.
Quote:”NASA Works to Give Satellite Swarms a Hive Mind”
Metinks that before they do any of that, they wanna get their Blue-Sky-Brain-Storms and Loose Cannons under some sort of control
(Sputnik Wreck doesn’t roll off the tongue as well as Train Wreck – but is an increasingly good definition of NASA)
Terrific! Now, if we only could find a smart young programmer willing to write an honest model. 😟
I was expecting to read “could vastly increase the debris pollution of orbital space.”
This sounds a bit like: “We don’t know how to program them to be more useful, but if we use AI, they can learn it themselves”.