Cat-Like Whiskers Help Tiny Drones Fly in the Dark

The “whiskers” Hamaza and her team crafted for the drone consist of delicate wires made of nickel-titanium, each measuring 0.4 millimeters in diameter—about the thickness of four sheets of paper. The antennae, however, were longer than the drone itself. To a palm-sized drone, the researchers attached two whiskers, each nearly 8 inches long with three small pressure sensors at the base. These can assess the bending of the wires, from which the drone estimates its distance from and position in relation to nearby barriers. All calculations necessary for this navigation are processed by a microcontroller onboard the drone, without relying on an external computer.

It may not sound too complicated, but even determining the angle at which to mount the wires was a challenge. Animals that navigate via whisker generally do not fly. Having a long protrusion, even if very light and delicate, can make the drone unstable. When moving along a wall, the researchers saw that resistance from the whisker could cause the drone to rotate and crash.

The researchers compared a total of 72 flights, testing six different angles at three different speeds. The drone flew without major incident when the whiskers were mounted at 30 degrees, 45 degrees, and 60 degrees. Of these, the 45-degree angle was adopted because it resulted in the least deviation in the drone’s orientation. They also took into account the fact that a 60-degree angle reduces the forward sensing range.

The researchers also encountered several hurdles in processing the information taken in by the whiskers. One was distinguishing between actual contact signals and disturbances caused by the drone itself, such as vibrations from the propellers. Even when filtering this noise out, about 38 percent of the drone’s detections were false positives.

The issue seemed to be drift, a phenomenon where sensor readings gradually shift over time. The research team, therefore, developed a correction algorithm that continuously estimates the trend of drifts over time—based on sensor readings when the drone is not touching anything—while in flight. When combined with the filter, false positives reportedly dropped to 0 percent.