Using Vibrations and AI, Future Robotic Missions Could Handle Buried Rocks Autonomously

Mechanical engineering assistant professor Erik Komendera developed a method that combines vibrations and AI to determine how deeply a rock is buried, helping robots clear terrain more efficiently. The science-journalism coverage adds useful context, while the strongest evidential footing still comes from the underlying data, papers or institutional documentation.

That matters because astronomy does not advance on single detections. The field builds confidence by accumulating independent observations across different wavelengths, instruments and epochs until isolated signals become defensible conclusions. What looks convincing in one dataset can dissolve when a second instrument looks at the same target, and what looks marginal can solidify when follow-up campaigns confirm the original reading. The current standard requires that a result survive this triangulation before the community treats it as settled. Thanks to a team from Virginia Polytechnic Institute and State University (Virginia Tech), scientists could soon have a new set of tools that will allow robotic missions to make. Candidate at Virginia Tech, and Erik Komendera, an assistant professor of mechanical engineering (and Ruan's advisor) who once worked as a researcher at NASA's Langley Research.

View generated from the digital terrain model and the nadir and color channels of the High Resolution Stereo Camera on ESA's Mars Express. ESA/DLR/FU Berlin As they indicate in their paper, estimating the burial depth of partially embedded rocks is a key capability for energy-limited robotic excavation.

As Komendera said in a Virginia Tech press release: For as long as humans have built things on Earth, we have had to deal with large and heavy obstacles that cannot simply be. They then struck the exposed portions of the blocks with an instrumented hammer and remotely measured the vibrations using a device known as a Laser Doppler Vibrometer (LDV).

The results were then input into a Multilayer Perceptron (MLP), a type of neural network that learns to identify complex, non-linear patterns from data. NASA/JPL "Estimating how deep a rock is buried using vibrations can be tricky," said Ruan.

What gives the story weight is not just the object itself, but the way the measurement trims the range of plausible physical explanations. Astronomy has accumulated enough cases to know that the most interesting results are rarely the ones that confirm expectations cleanly; they are the ones that confirm some expectations while complicating others, or that open a parameter space that previous instruments could not reach. The scientific community evaluates these contributions by asking whether the new data constrain a model in a way that older data could not, and whether those constraints survive systematic review.

In the meantime, the team plans to keep building datasets and testing their approach for applications on Earth, such as analyzing construction sites and natural disasters to. They could also be used to study unstable rock formations and underground caverns, assisting with resource and construction planning, and advancing exploration of Earth's.

Because this item comes through Universe Today as science journalism, it should be treated as contextual reporting rather than primary evidence. Good science reporting can identify why a result matters, connect it to the wider literature and make technical work readable, but the decisive evidence remains in the original paper, dataset, mission release or technical record. That distinction is especially important when a story is later repeated by aggregators, because repetition increases visibility, not evidential strength.

The next step is to see whether other instruments and other wavelengths tell the same story. Campaigns with JWST, the VLT, the forthcoming Extremely Large Telescopes and radio arrays will provide the spectral coverage and spatial resolution needed to move from detection to physical characterization. The timeline for that kind of confirmation is typically measured in years, not months, which is worth keeping in mind when reading the current result.

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