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In 2023, NASA plans to launch the Europa Clipper mission, a robotic explorer that will study Jupiter’s enigmatic moon Europa. The purpose of this mission is to explore Europa’s ice shell and interior to learn more about the moon’s composition, geology, and interactions between the surface and subsurface. Most of all, the purpose of this mission is to shed light on whether or not life could exist within Europa’s interior ocean.
This presents numerous challenges, many of which arise from the fact that the Europa Clipper will be very far from Earth when it conducts its science operations. To address this, a team of researchers from NASA’s Jet Propulsion Laboratory (JPL) and Arizona State University (ASU) designed a series of machine-learning algorithms that will allow the mission to explore Europa with a degree of autonomously.
Algorithms - Exploration - Missions - Subject - Presentation
How these algorithms might assist with future deep-space exploration missions were the subject of a presentation delivered last week (Aug. 7th) at the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining in Anchorage, Alaska. This annual conference brings researchers and practitioners in data science, data mining and analytics from all over the world together to discuss the latest developments and applications.
When it comes right down to it, communicating with deep-space missions is time-consuming, challenging work. When communicating with missions on the surface of Mars or in orbit, it can take a signal up to 25 minutes to reach them from Earth (or back again). Sending signals to Jupiter, on the other hand, can take between 30 minutes to up to an hour, depending on where it is in its orbit relative to Earth.
Authors - Study - Spacecraft - Activities - Script
As the authors note in their study, spacecraft activities are typically transmitted in a pre-planned script rather than through real-time commands. This approach is very effective when the position, environment, and other factors...
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Measuring his life out one teaspoon at a time.