{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:55:46Z","timestamp":1754157346283,"version":"3.41.2"},"reference-count":21,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2012,6,15]],"date-time":"2012-06-15T00:00:00Z","timestamp":1339718400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,6,15]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-heading\">Purpose<\/jats:title><jats:p>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\u2010positions 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.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Findings<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title><jats:p>The paper presents a novel approach to RFID sensor modelling using adaptive neuro\u2010fuzzy inference. The model uses both Received Signal Strength Indication (RSSI) 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.<\/jats:p><\/jats:sec>","DOI":"10.1108\/01439911211227908","type":"journal-article","created":{"date-parts":[[2014,5,9]],"date-time":"2014-05-09T06:59:12Z","timestamp":1399618752000},"page":"340-348","source":"Crossref","is-referenced-by-count":13,"title":["RFID tag localization by using adaptive neuro\u2010fuzzy inference for mobile robot applications"],"prefix":"10.1108","volume":"39","author":[{"given":"Grazia","family":"Cicirelli","sequence":"first","affiliation":[]},{"given":"Annalisa","family":"Milella","sequence":"additional","affiliation":[]},{"given":"Donato","family":"Di Paola","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2022020419473414400_b1","unstructured":"Alippi, C., Cogliati, D. and Vanini, G. (2006), \u201cA statistical approach to localize passive RFIDs\u201d, Proceedings of IEEE International Symposium on Circuits and Systems 2006, Island of Kos, Greece."},{"key":"key2022020419473414400_b2","doi-asserted-by":"crossref","unstructured":"Bezdek, J.C. (1981), Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum, New York, NY.","DOI":"10.1007\/978-1-4757-0450-1"},{"key":"key2022020419473414400_b3","doi-asserted-by":"crossref","unstructured":"Choi, B.S., Lee, J.W., Lee, J.J. and Park, K.T. (2011), \u201cA hierarchical algorithm for indoor mobile robots localization using RFID sensor fusion\u201d, IEEE Transactions on Industrial Electronics, Vol. 58 No. 6, pp. 2226\u201035.","DOI":"10.1109\/TIE.2011.2109330"},{"key":"key2022020419473414400_b4","doi-asserted-by":"crossref","unstructured":"Cicirelli, G., D'Orazio, T. and Distante, A. (2005), \u201cDifferent learning methodologies for vision\u2010based navigation behaviors\u201d, International Journal on Pattern Recognition and Artificial Intelligence, Vol. 19 No. 8, pp. 1\u201026.","DOI":"10.1142\/S021800140500440X"},{"key":"key2022020419473414400_b5","doi-asserted-by":"crossref","unstructured":"Di Paola, D., Milella, A., Cicirelli, G. and Distante, A. (2010), \u201cAn autonomous mobile robotic system for surveillance of indoor environments\u201d, International Journal of Advanced Robotic Systems, Vol. 7 No. 1, pp. 19\u201026 (special Issue on Robotics for Risky Interventions and Environment Surveillance).","DOI":"10.5772\/7254"},{"key":"key2022020419473414400_b6","doi-asserted-by":"crossref","unstructured":"Gramegna, T., Venturino, L., Cicirelli, G., Attolico, G. and Distante, A. 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