‘Spidey-senses’ could help autonomous vehicles better detect and avoid objects

Researchers are building animal-inspired sensors into the shells of aircraft, cars.


Spider-Man’s most famous ability is his “Spidey-sense,” which allows the hero to sense danger in advance and quickly react. It is not completely fiction- the Spiders have tiny sensitive hairs on their legs that help them avoid predators or hunt prey.

Now, Purdue University engineers have developed similar sensors that could be used in autonomous cars or drones. The team has built sensors inspired by spiders, bats, birds and other animals, whose actual Spidey-senses are nerve endings linked to special neurons called mechanoreceptors.

According to the team, these spider-inspired sensors can help the vehicles to better detect and avoid objects, because they would process sensory information faster.

Better sensing capabilities would make it possible for drones to navigate in dangerous environments and for cars to prevent accidents caused by human error. And the current sensor technology doesn’t process data fast enough.

The ‘mechanosensors’- the nerve endings like the leg-hairs of the spiders perfectly tuned to focus only on the data that the spider needs for survival. When a spider’s web vibrates at a frequency associated with prey or a mate, the mechanosensors detect it, generating a reflex in the spider that then reacts very quickly. The mechanosensors wouldn’t detect a lower frequency, such as that of dust on the web, because it’s unimportant to the spider’s survival.In nature, ‘spidey-senses’ are activated by a force associated with an approaching object. Researchers are giving autonomous machines the same ability through sensors that change shape when prompted by a predetermined level of force. (ETH Zürich images/Hortense Le Ferrand)

Inspired with this, the Purdue team create mechanosensors that will ignore minor forces and only signal the rest of the machine after that sensation hits a certain threshold. The sensors are made of the material that starts off stiff, but changes shape rapidly when an external force is applied to it. And when its changed shape reaches a certain point, conductive particles inside the material come together and allow electricity to flow through. That, in turn, sends a signal to the rest of the machine, which responds as needed.

With the help of machine learning algorithms, we could train these sensors to function autonomously with minimum energy consumption,” said Andres Arrieta, lead author of the study. “There are also no barriers to manufacturing these sensors to be in a variety of sizes.”

But the sensors they developed don’t just sense and filter at a very fast rate- they also require less energy and computational power to run.

These type of sensors could be placed on drones, planes or autonomous cars, which help them to detect objects and obstacles and avoid them much faster than is currently possible.

The results are published in the journal ACS Nano.

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