Skip to main content
  1. Home
  2. Emerging Tech
  3. News

MIT’s RFID drones could solve a multibillion-dollar problem — and find lost keys

Add as a preferred source on Google

When you’re dealing with the kind of giant warehouses required by retail giants and other large organizations, taking inventory of stock by hand can be an enormously time-consuming job that verges on the impossible. For example, even the smallest Walmart warehouse is larger than 17 football fields, making it easy for things to get lost. (This is actually more of a serious problem than you might think: over an eight-year period, the U.S. Army lost track of $5.8 billion of supplies in its warehouses.) As a result, companies have increasingly been looking into using drones to speed up the task. However, most attempts to do this haven’t been as efficient as they could be, primarily because they rely on barcode readers or cameras, which miss any items not visible to a camera through line of sight.

That’s an issue that Massachusetts Institute of Technology researchers have tried to address with a new project called RFly, which uses a combination of drones and RFID (radio frequency identifier) tags to, they hope, revolutionize both inventory management and the “non-line-of-sight” problem.

Image used with permission by copyright holder

“We developed RFly, a new technology that allows drones to find missing and hidden objects using wireless signals,” Fadel Adib, whose group at the MIT Media Lab developed the new system, told Digital Trends. “Our technology works by analyzing the wireless signals reflected from battery-free RFID stickers. RFIDs are wireless stickers that are attached to objects similar to barcodes. To locate these RFIDs, our drones transmit wireless signals to power them up, then analyze their responses. As these drones fly, they analyze the physical waves of the RFID responses and use these waves to locate the RFIDs. Our technology allows drones to pin down the location of an RFID to the exact shelf an item is on, and our location-finding algorithm is inspired by how airplane radars map the surface of the Earth.”

Recommended Videos

According to its creators, the RFly system can read RFID tags from more than 50 feet away and identify objects on shelves within 8 inches of their location. The system could also be used for doing more than just Identifying products.

“The applications are vast, and they range from doing remote inventory control in an entire warehouse to allowing people to find missing items at home,” Adib continued. “Imagine a future where each of us has a small miniature drone, and we dispatch the drone to fetch our keys, wallets, or glasses when we can’t find them.”

Luke Dormehl
I'm a UK-based tech writer covering Cool Tech at Digital Trends. I've also written for Fast Company, Wired, the Guardian…
Meta’s latest AI model is Muse Spark 1.1 and it can run your computer for you
meta-ai-chatbot-threads

AI assistants have gotten really good at answering questions and walking us through complicated tasks. But the next wave of AI is aiming for something much bigger: doing those tasks for us.

That's the idea behind Meta's new Muse Spark 1.1. Instead of simply telling you which buttons to click, the model is built to interact with your computer on your behalf. Whether it's searching across multiple websites, filling out forms, or switching between apps, Meta says Muse Spark 1.1 can navigate software much like a person would, choosing the fastest way to finish the job. It's a notable shift from purely conversational AI to AI designed to take action.

Read more
AI security cameras may soon recognize your walk before they recognize your face
A new AI gait system tracks body motion through skeletal keypoints, aiming at long-range identity checks where face scans and fingerprints fall short.
Security cam

Security cameras are built to look for faces. New research suggests they may soon have another target, the small habits buried in the way someone walks.

A paper published in the International Journal of Reasoning-based Intelligent Systems describes SKDMap-Net as a gait recognition system designed to identify people from walking video, even when the camera doesn’t get a clean look at their face. Instead of relying on a close-up scan, it studies how a body moves from frame to frame.

Read more
A 20-second 3D printer breakthrough comes with exactly the kind of catch science loves
The process can create complex microstructures far faster than some laser-based methods, but full 3D control is still a work in progress.
Aluminium, Smoke Pipe

A 3D printer that can make a structure in about 20 seconds sounds like a lab claim wearing a cape. The clever bit is real. The catch arrives before anyone starts dreaming about instant replacement parts.

University of Utah researchers have demonstrated a holographic 3D printing technique that hardens tiny structures in one exposure instead of building them layer by layer. That one-shot approach could avoid the weak, leaky seams that stacked printing can leave behind. For now, though, this is a tool for microstructures, not a shortcut to printing whatever object pops into your head.

Read more