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TLBO is employed to train the parameters of ANFIS structure for optimal trajectory and minimum travelling time to reach the goal. The obtained results using the suggested algorithm are validated by comparison with different results from other intelligent algorithms such as particle swarm optimization (PSO), invasive weed optimization (IWO), and biogeography-based optimization (BBO). At the end, the quality of the obtained results extracted from simulations affirms TLBO-based ANFIS as an efficient alternative method for solving the navigation problem of the mobile robot.<\/jats:p>","DOI":"10.1155\/2018\/3145436","type":"journal-article","created":{"date-parts":[[2018,3,5]],"date-time":"2018-03-05T18:30:48Z","timestamp":1520274648000},"page":"1-8","source":"Crossref","is-referenced-by-count":28,"title":["TLBO-Based Adaptive Neurofuzzy Controller for Mobile Robot Navigation in a Strange Environment"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5945-2309","authenticated-orcid":true,"given":"Awatef","family":"Aouf","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, National Engineering School of Sousse, University of Sousse, BP 264, Erriadh, 4023 Sousse, Tunisia"},{"name":"Laboratory E\u03bcE, Faculty of Sciences of Monastir (FSM), University of Monastir, Av. 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