Robots Track Moving Objects With Unprecedented Precision

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Adib’s group has been working for years on using radio signals for tracking and identification purposes, such as detecting contamination in bottled foods, communicating with devices inside the body, and managing warehouse inventory.

Similar systems have attempted to use RFID tags for localization tasks. But these come with trade-offs in either accuracy or speed. To be accurate, it may take them several seconds to find a moving object; to increase speed, they lose accuracy.

The challenge was achieving both speed and accuracy simultaneously. To do so, the researchers drew inspiration from an imaging technique called “super-resolution imaging.” These systems stitch together images from multiple angles to achieve a finer-resolution image.

“The idea was to apply these super-resolution systems to radio signals,” Adib says. “As something moves, you get more perspectives in tracking it, so you can exploit the movement for accuracy.”

The system combines a standard RFID reader with a “helper” component that’s used to localize radio frequency signals. The helper shoots out a wideband signal comprising multiple frequencies, building on a modulation scheme used in wireless communication, called orthogonal frequency-division multiplexing.

The system captures all the signals rebounding off objects in the environment, including the RFID tag. One of those signals carries a signal that’s specific to the specific RFID tag, because RFID signals reflect and absorb an incoming signal in a certain pattern, corresponding to bits of 0s and 1s, that the system can recognize.

Because these signals travel at the speed of light, the system can compute a “time of flight” — measuring distance by calculating the time it takes a signal to travel between a transmitter and receiver — to gauge the location of the tag, as well as the other objects in the environment. But this provides only a ballpark localization figure, not subcentimter precision.

Leveraging Movement

To zoom in on the tag’s location, the researchers developed what they call a “space-time super-resolution” algorithm.

The algorithm combines the location estimations for all rebounding signals, including the RFID signal, which it determined using time of flight. Using some probability calculations, it narrows down that group to a handful of potential locations for the RFID tag.

As the tag moves, its signal angle slightly alters — a change that also corresponds to a certain location. The algorithm then can use that angle change to track the tag’s distance as it moves. By constantly comparing that changing distance measurement to all other distance measurements from other signals, it can find the tag in a three-dimensional space. This all happens in a fraction of a second.

“The high-level idea is that, by combining these measurements over time and over space, you get a better reconstruction of the tag’s position,” Adib says.

“The work reports sub-centimeter accuracy, which is very impressive for RFID,” says Lili Qiu, a professor of computer science at the University of Texas at Austin whose research focuses on wireless networking and communications. “The paper proposes an interesting idea that lets a ‘helper’ transmit a wideband signal compatible with RFID protocol to achieve high tracking accuracy [and] develops a … framework for RF localization that fuses measurements across time and across multiple antennas. The system has potential to support [the researchers’] target applications, such as robotic assembly and nanodrones. … It would be very interesting to see the field test results in the future.”

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