WEST LAFAYETTE, Indiana — The oddly satisfying little domes you press onto the lid of your soda cup might one day save a winged drone from a nosedive.
Patterns of these reversible domes on a drone’s wings would allow it to remember in microseconds what dangerous conditions feel like and react quickly. The study, conducted by researchers from Purdue University and the University of Tennessee, Knoxville, is among the first demonstrations of a metamaterial that uses its shape to learn to adapt itself to its environment. The paper was published in the journal Advanced Intelligent Systems.
Unlike humans and other living things, autonomous vehicles lack the ability to filter out information they don’t need, which slows their response time to changes in their environment.
“There’s this problem called ‘data drowning.’ Drones cannot use their full flight capabilities because there is simply too much data to process from their sensors, preventing them from flying safely in certain situations,” said Andres Arrieta, an associate professor of mechanical engineering at Purdue University with a honors professorship Aerospace Engineering.
Domed surfaces that can sense their surroundings would be a step toward allowing a drone’s wings to sense only the most necessary sensory information. Since only a certain minimum force is required to invert a dome, forces below this threshold are automatically filtered out. For example, a certain combination of domes popping up and down on certain parts of the wing could indicate to the drone’s control system that the wing is experiencing a dangerous pressure pattern. Other dome patterns could indicate dangerous temperatures or that an object is approaching, Arrieta said.
Giving drones associative memory through feeling
It might seem strange that a reversible dome could give a drone wing reminder cues for dangerous conditions, but humans and animals also use unrelated concepts to recognize relationships. This learning strategy is called associative memory. For example, if you forget the name of a place, you can remember a detail like the color of a building. Retrieving a partial version of the memory allows you to construct a much more complete version of that memory.
Arrieta’s lab explores ways in which the shape of a technical material could help it calculate and process information. His lab is often inspired by how spiders and other animals use their anatomical forms to perceive and understand the world around them.
For decades, electronics have evolved to store and retrieve images by encoding information as zeros or ones in patterns of black or white pixels. Because a dome can only have two states—up or down—these states can act like zeros and ones to create spatial patterns for building associative memory.
Arrieta and his team showed in the study that when a specific force inverts a dome, sensors embedded in the flat portion of a metamaterial sheet surrounding the dome detect the change in shape. An electrical signal then triggers a memory device called a memristor to record the force and where it was detected on the blade. With each instance of an inverted dome, the metamaterial learns to remember the pattern that creates a certain level of force on its surface.
In practice, a drone wing would be able to quickly recall a pattern associated with a dangerous condition because the metamaterial records all of its “partial memories” of inverted dome patterns as a single “complete memory” that combines those patterns together generate. Based on this study, the researchers believe that the metamaterial would not need to “buffer” to retrieve information that it stores within itself over time.
Because the metamaterial can be produced using existing methods, these domes can easily cover a large surface area like a drone’s wing, Arrieta said. Next, researchers will test how the material responds to its environment based on information it learns from the domes. Arrieta believes that it will be possible to build a drone wing with this material design in the next three to five years.
This research is supported by the Defense Advanced Research Projects Agency, the National Science Foundation, and the Indiana Space Grant Consortium.
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Author/media contact: Kayla Wiles, 765-494-2432, [email protected]
Source: Andres Arrieta, [email protected]
Neuromorphic metamaterials for mechanosensorics and perceptual-associative learning
Advanced intelligent systems
The abstract and author list are available online.