The small domes on your soda’s to-go cup lids – that can be popped down to indicate what sort of beverage is in the cup – may one day save a winged drone from a nosedive.
Researchers at Purdue University and the University of Tennessee, Knoxville, have discovered that the patterns of these invertible domes on a drone’s wings give it a way to remember in microseconds what dangerous conditions feel like and react quickly.
Unlike animals, autonomous vehicles lack ways to filter out information they don’t need, which shows their response time to changes in their environment. “There’s this problem called ‘data drowning.’ Drones cannot use their full flight capability because there is just too much data to process from their sensors, which prevents them from flying safely in certain situations,” said Andres Arrieta, a Purdue associate professor of mechanical engineering.
The team of researchers has developed a proof-of-concept model in which a grid of cup-lid-like polyurethane domes is mounted on the top surface of a section of an aircraft wing. The prototype system comprises a multistable metamaterial whose bistable dome-shaped units collectively filter, amplify, and transduce external mechanical inputs over large areas into simple electrical signals using embedded piezoresistive sensors.
The dome-covered surfaces that can sense their surroundings would be a step toward enabling a drone‘s wings to feel only the most necessary sensory information. For example, a specific combination of domes popping up and down at 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 signify dangerous temperatures or that an object is approaching, Arrieta say.
A dome can adopt only two states – popped up or popped down – these states can act like zeros and ones to create spatial patterns for building associative memory. The study showed that when a certain level of force inverts a dome, sensors embedded into the flat part of a metamaterial sheet surrounding the dome detect the change in shape. An electrical signal then triggers a memory device called a memristor to make a record of the force and where it was detected on the sheet. With each instance of an inverted dome, the metamaterial learns to remember the pattern that a certain level of force creates on its surface.
In practice, if the dome pattern is associated with a dangerous condition, the drone’s flight control system would be advised so it could react accordingly. Researchers believe that the metamaterial wouldn’t need to “buffer” to recall information that it stores within itself over time.
Next, the researchers will test how the material responds to its surroundings based on the information it learns from the domes. Arrieta believes that it will be possible to build a drone wing using this material design in the next three to five years.
- Katherine S. Riley, Subhadeep Koner, Juan C. Osorio, Yongchao Yu, Harith Morgan, Janav P. Udani, Stephen A. Sarles, and Andres F. Arrieta. Neuromorphic Metamaterials for mechanosensing and perceptual associative learning. Advanced Intelligent Systems, 2022; DOI: 10.1002/aisy.202200158