Path Following with RFID tags in Unknown Environments

Ran Liu

Due to its simple, reliable and contactless way of identifying products, RFID has become an emerging technology and thus is already used in many industrial environments, like warehouses, stores or even libraries. Given the present infrastructure, RFID navigation features a cost-effective method if the robot is equipped with an RFID reader for inventory tasks. Therefore, we propose a new method employing a combination of RFID and odometry measurements for path following purposes.

In particular, we apply the teaching and playback scheme to perform this task, which has already been successfully used in different navigation systems with various sensors. During the teaching stage, the robot is manually controlled to move along a desired path. RFID measurements and the associated motion information are recorded in an online-fashion as reference data in this phase. In the second stage, the robot shall follow this path autonomously. Therefore, we compare current RFID measurements to the previously recorded reference data to estimate the robot's relative position (see Figure 2). As a result, motion control commands are generated by fusing the position and reference motion data to steer the robot. The approach needs no prior information about RFID sensor models, the distribution and positioning of the tags nor does it require a map of the environment. Particularly, it is adaptive to different reader power levels and various tag densities, which have a major impact on RFID performance.

Figure 1: The Scitos G5 robot used for our experiments.

Figure 2: Visualization of the orientation estimation of our path following approach. (Left) Robot following path with two antennas (orange, purple), RFID tags (green), reference fingerprints (blue) and the closest reference fingerprints (red box). (Right) Respective similarities of left and right antenna, that are used to estimate the index difference as well as the overall weighted similarity.

An example of an actual trajectory, ground truth and the odometry under full power are shown in Figure 3. Due to its cumulative characteristic, the localization accuracy of odometry will get far worse for longer tracks. Compared with the raw odometry trajectory, our approach is more precise. This can be seen in the hallway environment (right figure in Figure 3), where the mean error of odometry grows to 0.9 m while the error of our approach is only 0.2 m.

Figure 3: Ground truth, raw odometry data, and actual trajectory of the robot in a library (Left) and hallway environment (Right)

In the future, we plan to investigate the impact of tag relocations on our algorithm. In addition, we are going to enhance the sample frequency of the RFID reader to improve the recording speed of the robot and thus minimize the efforts for the training stage. We also plan to investigate practical applications of the approach in regard to topological maps. There, paths between nodes could be used to connect distinct places through edges. A navigation from a starting node to a goal node could be achieved by traversing the appropriate edges, which would enable the robot to reach arbitrary predefined places by graph-based path planning in large-scale environments.


[1] Ran Liu, Artur Koch, and Andreas Zell. Mapping UHF RFID Tags with a Mobile Robot using 3D Sensor Model. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013), Big Sight, Tokyo, Japan, November 2013. [ details | pdf ]
[2] Ran Liu, Artur Koch, and Andreas Zell. Path following with passive UHF RFID received signal strength in unknown environments. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2012), Vilamoura, Algarve, Portugal, October 2012. [ details | pdf ]
[3] Ran Liu, Philipp Vorst, Artur Koch, and Andreas Zell. Path following for indoor robots with RFID received signal strength. In The 19th International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2011), Split, Hvar, and Dubrovnik, Croatia, September 2011. (Best paper award at the Symposium on RFID Technologies and Internet of Things). [ details | pdf ]


Ran Liu, Tel.: +49 7071 29 78985
ran.liu at