The RFID tag system has received a lot of attention for ubiquitous computing. An RFID tag is attached to an object. With the unique ID of the RFID tag, a user identifies the object provided with the RFID tag and derives appropriate information about the object. One important application in the RFID technology is localizing RFID tags, which can be very useful in acquiring the position information concerning the RFID tags. It can be applied to navigation systems and positional detection systems for mobile robots. This paper proposes a new adaptive multi-range-sensing method for 3D localization of passive RFID tags by using a probabilistic approach. In this method, a mobile object (human, robot, etc.) with an RFID reader estimates the positions of RFID tags with multiple communication ranges dynamically. The effectiveness of the proposed method was demonstrated in experiments.

This paper presents an autonomous navigation system for a mobile robot using randomly distributed passive RFID tags. In the case of randomly distributed RFID tags, it is difficult to provide the precise location of the robot especially in the area of sparse RFID tag distribution. This, combined with the wide turning radius of the robot, can cause the robot to enter a zigzag exploration path and miss the goal. In RFID-based navigation, the key is to reduce both the number of RFID tags and the localization error for practical use in a large space. To cope with these, we utilized the Read time, which measures the reading time of each RFID tag. With this, we could estimate accurately the localization and orientation without using any external sensors or increasing the RFID tags. The average estimation errors of 7.8 cm in localization and 11 degrees in orientation were achieved with 102 RFID tags in the area of 4.2 m by 6.2 m. Our proposed method is verified with the path trajectories produced during navigation compared with conventional approaches.

To reduce RFID tag identification delay, we propose a novel Dynamic Splitting protocol (DS) which is an improvement of the Query tree protocol (QT). DS controls the number of branches of a tree dynamically. An improved performance of DS relative to QT is verified by analytical results and simulation studies.