A system was developed that uses radio-frequency identification (RFID) tags to help robots target moving objects with unprecedented speed and accuracy. The system could enable greater collaboration and precision by robots working on packaging and assembly, and by swarms of drones carrying out search-and-rescue missions. Robots using the system can locate tagged objects within 7.5 milliseconds, on average, and with an error of less than a centimeter.
In the TurboTrack system, 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 localization accuracy.
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 visualization, 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, which was held by another robotic arm. In another demonstration, the researchers tracked RFID-equipped nanodrones during docking, maneuvering, 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 system combines a standard RFID reader with a “helper” component that localizes 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.
To zoom in on the tag’s location, the researchers developed a “space-time super-resolution” algorithm that 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.
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