Supersensitive Accelerometer Could Be the Answer to Better Drone Control

Supercapacitor technology could make tiny accelerometers as much as 1 million times more sensitive

3 min read
Photograph of the sensor, with the top electrode removed. It looks like a black and white cube with a drop of water bulging from the top. The dimensions are 3 × 3 × 3 mm.
Photo: Ezzat Bakhoum

Accelerometers are everywhere. You’ve probably got at least one on your person right now. But today’s run-of-the-mill accelerometers—MEMS devices that measure a minute change in capacitance—just aren’t very sensitive. They’re built to fit into smartwatches and smaller things, and that small size hampers how well they can sense changes. Engineers in Florida have now come up with a new take on the accelerometer that is as much as 1 million times as sensitive as a typical smartphone accelerometer, and it maintains that sensitivity up to a car-crash-scale 100 gs.

That combination of high sensitivity and large dynamic range in a cube that’s just 3 millimeters on a side should make the new accelerometer particularly useful in things that move quickly in three-dimensions, such as military drones, microrobots, and self-guided projectiles, according its inventors.

Ordinary MEMS accelerometers are made up of a moveable plate and a stationary plate, oriented perpendicular to each dimension measured. Together, the plates form a capacitor. When the device accelerates, the moveable plate bends toward or away from the stationary plate, changing the capacitance. Because the plate can’t move far, the change in capacitance is pretty small. “This is why the sensitivity is extremely poor,” says Ezzat G. Bakhoum, associate professor at the University of West Florida, in Pensacola.

Bakhoum is an expert in supercapacitors (also called ultracapacitors). These are devices that, like a battery, store much more energy than a capacitor. Yet they can charge and discharge quickly, like a capacitor. So it was a natural move to try to make capacitive accelerometers “super.”

Supercapacitors replace the capacitor’s plates with a high surface-area material—carbon nanotubes in this case. Between the material is an electrolyte—tetraethylammonium tetrafluoroborate dissolved in propylene carbonate (an-off-the-shelf solution despite its impenetrable name). Supercapacitors store more energy because of the greater surface area and because charge is also stored in ions of the liquid.

SEM photograph of multiwalled CNTs of an average diameter of 250 nm and a length of about 20 \u03bcm, grown on a stainless steel electrode. 20-micrometer tall carbon nanotubes coat the inner surfaces of the accelerometer. Image: Ezzat Bakhoum

Bakhoum and his team built a sort of three-dimensional supercapacitor to act as an accelerometer. They started with a millimeter scale box, the inner walls of which were carbon-nanotube-coated stainless steel. Inside the cube, they placed a drop of the electrolyte. Because nanotubes are hydrophobic they repelled the electrolyte, shaping it into a ball that barely contacts all six of the cube’s walls.

At rest, the capacitance across any pair of the walls is basically zero, because the electrolyte isn’t really even touching the nanotubes. But an acceleration in any direction will squash the electrolyte down, driving it into the nanotubes opposite the direction of the acceleration and into the nanotubes of neighboring walls as well. This basically forms supercapacitors between the walls. Measuring their individual capacitances, Bakhoum’s group found, gives an accurate measure of acceleration.

Photograph of the experimental arrangement. The interface circuit and the sensor (indicated by the red arrow) are mounted inside an open-cavity IC chip (the wire bonding was added after the photograph was taken). The accelerometer attached to a test IC used to interpret its capacitance measurements. Photo: Ezzat Bakhoum

The device gives an accuracy of 75 nanofarads per g, compared to the femtofarads per g of typical capacitive accelerometers, according to Bakhoum.

His team reported its results in the latest issue of IEEE Transactions on Components, Packaging, and Manufacturing Technology.

Bakhoum says he’s discussed the work with industry interests considering commercializing the device. But for him, it’s time to move on to making other kinds of sensors “super.”

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How the U.S. Army Is Turning Robots Into Team Players

Engineers battle the limits of deep learning for battlefield bots

11 min read
Robot with threads near a fallen branch

RoMan, the Army Research Laboratory's robotic manipulator, considers the best way to grasp and move a tree branch at the Adelphi Laboratory Center, in Maryland.

Evan Ackerman
LightGreen

This article is part of our special report on AI, “The Great AI Reckoning.

"I should probably not be standing this close," I think to myself, as the robot slowly approaches a large tree branch on the floor in front of me. It's not the size of the branch that makes me nervous—it's that the robot is operating autonomously, and that while I know what it's supposed to do, I'm not entirely sure what it will do. If everything works the way the roboticists at the U.S. Army Research Laboratory (ARL) in Adelphi, Md., expect, the robot will identify the branch, grasp it, and drag it out of the way. These folks know what they're doing, but I've spent enough time around robots that I take a small step backwards anyway.

The robot, named RoMan, for Robotic Manipulator, is about the size of a large lawn mower, with a tracked base that helps it handle most kinds of terrain. At the front, it has a squat torso equipped with cameras and depth sensors, as well as a pair of arms that were harvested from a prototype disaster-response robot originally developed at NASA's Jet Propulsion Laboratory for a DARPA robotics competition. RoMan's job today is roadway clearing, a multistep task that ARL wants the robot to complete as autonomously as possible. Instead of instructing the robot to grasp specific objects in specific ways and move them to specific places, the operators tell RoMan to "go clear a path." It's then up to the robot to make all the decisions necessary to achieve that objective.

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