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Sampling-based online motion planning for mobile robots: utilization of Tabu search and adaptive neuro-fuzzy inference system

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dc.citedby9
dc.contributor.authorKhaksar W.en_US
dc.contributor.authorHong T.S.en_US
dc.contributor.authorSahari K.S.M.en_US
dc.contributor.authorKhaksar M.en_US
dc.contributor.authorTorresen J.en_US
dc.contributor.authorid54960984900en_US
dc.contributor.authorid8231495000en_US
dc.contributor.authorid57218170038en_US
dc.contributor.authorid55350135000en_US
dc.contributor.authorid6602518300en_US
dc.date.accessioned2023-05-29T07:27:00Z
dc.date.available2023-05-29T07:27:00Z
dc.date.issued2019
dc.descriptionAdaptive control systems; Controllers; Fuzzy neural networks; Fuzzy systems; Motion planning; Robot programming; Robots; Tabu search; Adaptive neuro fuzzy inference systems (ANFIS); Adaptive neuro-fuzzy inference system; ANFIS; Fuzzy controllers; Motion planning algorithms; Sampling-based algorithms; Sampling-based motion planning; Unknown environments; Fuzzy inferenceen_US
dc.description.abstractDespite the proven advantages of sampling-based motion planning algorithms, their inability to handle online navigation tasks and providing low-cost solutions make them less efficient in practice. In this paper, a novel sampling-based algorithm is proposed which is able to plan in an unknown environment and provides solutions with lower cost in terms of path length, runtime and stability of the results. First, a fuzzy controller is designed which incorporates the heuristic rules of Tabu search to enable the planner for solving online navigation tasks. Then, an adaptive neuro-fuzzy inference system (ANFIS) is proposed such that it constructs and optimizes the fuzzy controller based on a set of given input/output data. Furthermore, a heuristic dataset generator is implemented to provide enough data for the ANFIS using a randomized procedure. The performance of the proposed algorithm is evaluated through simulation in different motion planning queries. Finally, the proposed planner is compared to some of the similar motion planning algorithms to support the claim of superiority of its performance. � 2017, The Natural Computing Applications Forum.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s00521-017-3069-6
dc.identifier.epage1289
dc.identifier.scopus2-s2.0-85025161177
dc.identifier.spage1275
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85025161177&doi=10.1007%2fs00521-017-3069-6&partnerID=40&md5=e8bc23be0e7962aa4107b82f199b0302
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24784
dc.identifier.volume31
dc.publisherSpringer Londonen_US
dc.sourceScopus
dc.sourcetitleNeural Computing and Applications
dc.titleSampling-based online motion planning for mobile robots: utilization of Tabu search and adaptive neuro-fuzzy inference systemen_US
dc.typeArticleen_US
dspace.entity.typePublication
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