Nature-Inspired Sensors Boost Autonomous Machines' Vision for Safer Navigation

Better sensing capabilities would make it possible for drones to navigate in dangerous environments and for cars to prevent accidents caused by human error. Current state-of-the-art sensor technology doesn’t process data fast enough, but nature does.

Researchers have built sensors inspired by spiders, bats, birds, and other animals whose actual senses are nerve endings linked to special neurons called mechanoreceptors. The nerve endings — mechanosensors — only detect and process information essential to an animal’s survival. They come in the form of hair, cilia, or feathers. Many biological mechanosensors filter data — the information they receive from an environment — according to a threshold such as changes in pressure or temperature.

A spider’s hairy mechanosensors, for example, are located on its legs. 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.

The idea would be to integrate similar sensors straight into the shell of an autonomous machine such as an air-plane wing or the body of a car. The mechanosensors could be customized to detect predetermined forces that would be associated with a certain object that an autonomous machine needs to avoid.

In addition to sensing and filtering at a very fast rate, the sensors also compute without needing a power supply. In nature, once a particular level of force activates the mechanoreceptors associated with the hairy mechanosensor, the mechanoreceptors compute information by switching from one state to another. The new sensors do the same and use these on/off states to interpret signals. An intelligent machine would then react according to what the sensors compute.

The artificial mechanosensors are capable of sensing, filtering, and computing very quickly because they are stiff. The sensor material is designed to rapidly change shape when activated by an external force. Changing shape makes conductive particles within the material move closer to each other, which then allows electricity to flow through the sensor and carry a signal. This signal informs how the autonomous system should respond.

Using machine learning algorithms, the sensors could be trained to function autonomously with minimum energy consumption. There also would be no barriers to manufacturing the sensors in a variety of sizes.