New RFID tags help robots to track moving objects

MIT scientists have developed a novel system that uses radio-frequency identification (RFID) tags to help robots home in on moving objects with unprecedented speed and accuracy.

The system, called TurboTrack, could enable greater collaboration and precision by robots working on packaging and assembly, and by swarms of drones carrying out search-and-rescue missions.

The researchers at Massachusetts Institute of Technology (MIT) in the US show that robots using the system can locate tagged objects within 7.5 milliseconds, on average, and with an error of less than a centimetre.

TurboTrack, an RFID tag can be applied to any object. A reader sends a wireless signal that reflects off the RFID tag and other nearby objects, and rebounds to the reader.

An algorithm sifts through all the reflected signals to find the RFID tag’s response.

Final computations then leverage the RFID tag’s movement -- even though this usually decreases precision -- to improve its localisation accuracy.

The researchers said the system could replace computer vision for some robotic tasks. As with its human counterpart, computer vision is limited by what it can see, and it can fail to notice objects in cluttered environments.

Radio frequency signals have no such restrictions: They can identify targets without visualisation, within clutter and through walls.

To validate the system, the researchers attached one RFID tag to a cap and another to a bottle. A robotic arm located the cap and placed it onto the bottle, held by another robotic arm.

In another demonstration, the researchers tracked RFID-equipped nanodrones during docking, manoeuvring and flying.

In both tasks, the system was as accurate and fast as traditional computer-vision systems, while working in scenarios where computer vision fails, the researchers said.

“If you use RF signals for tasks typically done using computer vision, not only do you enable robots to do human things, but you can also enable them to do superhuman things,” said Fadel Adib, an assistant professor in the MIT Media Lab.

“And you can do it in a scalable way, because these RFID tags are only 3 cents each,” Adib said.

In manufacturing, the system could enable robot arms to be more precise and versatile in, say, picking up, assembling, and packaging items along an assembly line.

Another promising application is using handheld “nanodrones” for search and rescue missions. Nanodrones currently use computer vision and methods to stitch together captured images for localisation purposes.

These drones often get confused in chaotic areas, lose each other behind walls, and can’t uniquely identify each other. This all limits their ability to, say, spread out over an area and collaborate to search for a missing person, researchers said.

Using the system, nanodrones in swarms could better locate each other, for greater control and collaboration.

“You could enable a swarm of nanodrones to form in certain ways, fly into cluttered environments, and even environments hidden from sight, with great precision,” said Zhihong Luo, a graduate student in the Signal Kinetics Research Group at MIT.

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