This paper describes a UHF-RFID robotic system for tagged-item inventory and localization. A mobile robot is equipped with wheeled rotary encoders and a UHF-RFID reader connected to multiple antennas. At first, the robot reconstructs its trajectory by exploiting a sensor-fusion method combining odometry data with phase data gathered by on-board antennas from an infrastructure of passive reference tags. Then, it leverages its reconstructed trajectory to localize target tags placed at unknown locations through a multi-antenna synthetic-aperture-radar (SAR) approach. A Particle Swarm Optimization is applied to speed up the position estimation. An experimental campaign conducted in an office environment is presented to verify the system features and feasibility. The performance of the proposed method for both tag localization and robot self-localization is compared with respect to the case of trajectories reconstructed only by odometry data or through a commercial Laser Range Finder mounted on the robot. Particularly, the effect of the cumulated drift of the estimated trajectory on the tag localization performance is investigated.

A UHF-RFID Multi-Antenna Sensor Fusion Enables Item and Robot Localization

Motroni A.
Primo
;
Bernardini F.;Buffi A.;Nepa P.;Tellini B.
2022-01-01

Abstract

This paper describes a UHF-RFID robotic system for tagged-item inventory and localization. A mobile robot is equipped with wheeled rotary encoders and a UHF-RFID reader connected to multiple antennas. At first, the robot reconstructs its trajectory by exploiting a sensor-fusion method combining odometry data with phase data gathered by on-board antennas from an infrastructure of passive reference tags. Then, it leverages its reconstructed trajectory to localize target tags placed at unknown locations through a multi-antenna synthetic-aperture-radar (SAR) approach. A Particle Swarm Optimization is applied to speed up the position estimation. An experimental campaign conducted in an office environment is presented to verify the system features and feasibility. The performance of the proposed method for both tag localization and robot self-localization is compared with respect to the case of trajectories reconstructed only by odometry data or through a commercial Laser Range Finder mounted on the robot. Particularly, the effect of the cumulated drift of the estimated trajectory on the tag localization performance is investigated.
2022
Motroni, A.; Bernardini, F.; Buffi, A.; Nepa, P.; Tellini, B.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1158371
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