An RFID-Based Mobile Robot Localization Method Combining Phase Difference and Readability

Abstract

A novel radio frequency identification (RFID)-based mobile robot global localization method combining two kinds of RFID signal information, i.e., phase difference and readability, is proposed. Specifically, a phase difference model and a classification logic strategy based on readability are built and integrated into a particle filter localization algorithm. Compared with existing RFID localization methods, the proposed localization method can achieve competitive localization performance in an environment with a relatively sparse reference tag distribution and without the need for offline phase drift calibration. A series of real experimental tests were performed, and the results show that the proposed method can localize a mobile robot with centimeter-level position accuracy and satisfactory attitude angle accuracy when the distance between adjacent reference tags is approximately 60 cm, even if all RFID devices are commercial off-the-shelf (COTS). The proposed method provides a promising option for mobile robot localization applications, such as path tracking of mobile robots. Note to Practitioners —Mobile robot localization is a key technology for its location-based services. Considering that radio frequency identification (RFID) is entirely unaffected by light interference and has a globally unique ID, RFID has been regarded as a localization sensor with broad application prospects. This article proposes an RFID-based mobile robot global localization method combining phase difference and readability, by which the mobile robot can be accurately localized in an environment with a relatively sparse reference tag distribution and without the need for offline phase drift calibration. The experimental results indicate that the proposed method can localize the mobile robot with good performance, including centimeter-level position accuracy and satisfactory attitude angle accuracy. The proposed method can effectively contribute to many practical applications, such as the path tracking of a mobile robot.