RFID-Based Robots Come to the Rescue

Using Active RFID Sensors
The University of Freiburg's department of microsystems engineering developed wireless sensor nodes that meet the ZigBee specification for an experiment with active RFID. The sensors measure air pressure, temperature and antenna orientation. "The sensor node houses a two-axis accelerometer that measures gravity in order to determine the orientation of the antenna," Kleiner says. "Thanks to this information, the sensor detects when the antenna is not appropriately orientated—i.e., if it has fallen over."

During the experiment, nine active RFID tags with sensors were mounted outdoors on traffic pylons, with the tag's orientation fixed. Four robots navigated the area, one after another, reading tags and leaving behind information for other robots. All of this data was then collected and processed to create a map.

Kleiner's team used passive HF tags attached to buildings or embedded in the ground.

By using the information obtained from the other robots, Kleiner says, single robots were able to improve their maps compared with those based on their own observations, which had a margin of error of 2 meters (6.6 feet). "You can imagine this as follows: The first robot drives around and computes a map from dead reckoning and RFID observations," he explains. "This map is better than the one from dead reckoning alone. The resulting map is stored on each nearby RFID tag. Then, the second robot goes through the area and does the same as the first one. However, it also reads the information from the previous robot and creates an even more accurate map."

The robots detected the active RFID signals 30 meters (98 feet) from tags, and created a map with an error of 1 meter (3.3 feet). "Here the accuracy depends mainly on the accuracy of estimating the distance to RFID tags, based on the signal strength," Kleiner says.

"We learned from our experiments, the environment and the arrangement of the tags significantly influence signal path attenuation. These parameters have to be predicted for reliable distance estimation from signal strength," Kleiner says. "To apply the method in indoor environments would be more difficult because signals can be reflected by objects and walls, leading to signal propagations via different paths, known as the 'multi- path propagation' problem." It is possible, he notes, to use the RFID system indoors if the 3-D model is known beforehand. Robots can learn specific models for the purpose of locating people inside a building.

Companies have begun showing an interest in the idea, Kleiner says, and he hopes to obtain more funding through the European Union, since his system of mapping can also be employed for such security applications as monitoring nuclear plants with robot teams.

"I'm sure that anyone researching SLAM methods will see that RFID is the most promising solution from a practical point of view," he says, "since the inherent assumptions for most SLAM methods are not present in a disaster situation. Cameras don't help rescue teams because they can't see through smoke. GPS fails completely when satellite signals are blocked by structures made of reinforced concrete. And 3-D laser measurement techniques are still too expensive."