Purdue University researchers are engineering sensors inspired by spiders, bats, birds, and other animals in order to give drones and other autonomous machines spider-like senses that help them better navigate their environments.
The sensors function like real nerve endings linked to special neurons called mechanoreceptors that process information essential to an animal's survival.
An explosion of data
"There is already an explosion of data that intelligent systems can collect -- and this rate is increasing faster than what conventional computing would be able to process," said Andres Arrieta, an assistant professor of mechanical engineering at Purdue University.
"Nature doesn't have to collect every piece of data; it filters out what it needs," he said.
Biological mechanosensors are experts at filtering data. A spider's hairy mechanosensors located on its legs, for instance, allow it to react very quickly when it senses a threat or a potential mate.
However, the mechanosensors naturally ignore a lower frequency, such as dust, because it's not relevant to the spider's survival. Taking inspiration from this process, the team of scientists developed similar engineered sensors that can be incorporated into drones or even planes and cars.
These mechanosensors could be programmed to detect predetermined forces such as objects that autonomous machine need to avoid. The machines could then react at a faster more instinctive rate. Even more impressively, the team further engineered their sensors to also compute.
Hardware and software in nature
"There's no distinction between hardware and software in nature; it's all interconnected," Arrieta said. "A sensor is meant to interpret data, as well as collect and filter it."
The novel artificial mechanosensors can use on/off states to interpret signals and then guide an intelligent machine to the appropriate course of action. Furthermore, these advanced sensors can sense, filter, and compute very quickly because they are stiff.
The sensor material is created to rapidly change shape when activated by an external force using electricity to send a signal. This is achieved when the changing shape drives the conductive particles within the material to move closer to each other.
This allows electricity to flow through the sensor and carry a signal that then serves to inform how the autonomous system should respond. Better yet, they undertake this whole process with little power required.
"With the help of machine learning algorithms, we could train these sensors to function autonomously with minimum energy consumption," Arrieta said. "There are also no barriers to manufacturing these sensors to be in a variety of sizes."
The study is published in ACS Nano.