RFID tag localization by using adaptive neuro-fuzzy inference for mobile robot applications

Purpose - The purpose of this paper is to address the use of passive RFID technology for the development of an autonomous surveillance robot. Passive RFID tags can be used for labelling both valued objects and goal-positions that the robot has to reach in order to inspect the surroundings. In addition, the robot can use RFID tags for navigational purposes, such as to keep track of its pose in the environment. Automatic tag position estimation is, therefore, a fundamental task in this context. Design/methodology/approach - The paper proposes a supervised fuzzy inference system to learn the RFID sensor model; Then the obtained model is used by the tag localization algorithm. Each tag position is estimated as the most likely among a set of candidate locations. Findings - The paper proves the feasibility of RFID technology in a mobile robotics context. The development of a RFID sensor model is first required in order to provide a functional relationship between the spatial attitude of the device and its responses. Then, the RFID device provided with this model can be successfully integrated in mobile robotics applications such as navigation, mapping and surveillance, just to mention a few. Originality/value - The paper presents a novel approach to RFID sensor modelling using adaptive neuro-fuzzy inference. The model uses both Received Signal Strength Indication (MI) and tag detection event in order to achieve better accuracy. In addition, a method for global tag localization is proposed. Experimental results prove the robustness and reliability of the proposed approach.