Behold the "Smellicopter": A wee drone that's been outfitted with a live antenna from a moth, which lets it navigate towards smells.
It worked — and when they compared its performance to an artificial, human-made smell sensor, the moth antenna won out: The drone navigated more quickly towards an odor.
Why try to improve upon nature using biomimickry, when you can use nature itself? Judged on the arid plains of engineering innovation, this is clever as heck.
Judged using a more Freudian standard, this experiment fairly trembles with uncanniness. There's something so very unsettling about seeing a cyborg robot outfitted with live moth parts. Apparently the moth antenna remains active for four hours after it's been removed from the insect (they anesthetize the moths, BTW). To get a signal out of it they just run an electrical current though it. In a way, it's this latter detail that I find most morbidly fascinating: That parts of our biology work perfectly fine as severable, discrete plug-in boards for electronics, even adapting nicely to the voltage specs of microprocessors.
I don't want to harsh on this experiment too much — it's genuinely interesting. And as they point out, improving olfactory sensing has some very practical uses, including sniffing out the unexploded ordinances that ruin people's lives in former combat zones.
"Nature really blows our human-made odor sensors out of the water," said lead author Melanie Anderson, a UW doctoral student in mechanical engineering. "By using an actual moth antenna with Smellicopter, we're able to get the best of both worlds: the sensitivity of a biological organism on a robotic platform where we can control its motion."
The moth uses its antennae to sense chemicals in its environment and navigate toward sources of food or potential mates.
"Cells in a moth antenna amplify chemical signals," said co-author Thomas Daniel, a UW professor of biology who co-supervises Anderson's doctoral research. "The moths do it really efficiently — one scent molecule can trigger lots of cellular responses, and that's the trick. This process is super efficient, specific and fast." [snip]
"From a robotics perspective, this is genius," said co-author and co-advisor Sawyer Fuller, a UW assistant professor of mechanical engineering. "The classic approach in robotics is to add more sensors, and maybe build a fancy algorithm or use machine learning to estimate wind direction. It turns out, all you need is to add a fin."