Adapted-RRT: novel hybrid method to solve three-dimensional path planning problem using sampling and metaheuristic-based algorithms

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dc.contributor.authorKiani, Farzad
dc.contributor.authorSeyyedabbasi, Amir
dc.contributor.authorAliyev, Royal
dc.contributor.authorGulle, Murat Ugur
dc.contributor.authorBasyildiz, Hasan
dc.contributor.authorShah, M. Ahmed
dc.date.accessioned2024-03-13T10:30:43Z
dc.date.available2024-03-13T10:30:43Z
dc.date.issued2021
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description.abstractThree-dimensional path planning for autonomous robots is a prevalent problem in mobile robotics. This paper presents three novel versions of a hybrid method designed to assist in planning such paths for these robots. In this paper, an improvement on Rapidly exploring Random Tree (RRT) algorithm, namely Adapted-RRT, is presented that uses three well-known metaheuristic algorithms, namely Grey Wolf Optimization (GWO), Incremental Grey Wolf Optimization (I-GWO), and Expanded Grey Wolf Optimization (Ex-GWO)). RRT variants, using these algorithms, are named Adapted-RRTGWO, Adapted-RRTI-GWO, and Adapted-RRTEx-GWO. The most significant shortcoming of the methods in the original sampling-based algorithm is their inability in finding the optimal paths. On the other hand, the metaheuristic-based algorithms are disadvantaged as they demand a predetermined knowledge of intermediate stations. This study is novel in that it uses the advantages of sampling and metaheuristic methods while eliminating their shortcomings. In these methods, two important operations (length and direction of each movement) are defined that play an important role in selecting the next stations and generating an optimal path. They try to find solutions close to the optima without collision, while providing comparatively efficient execution time and space complexities. The proposed methods have been simulated employing four different maps for three unmanned aerial vehicles, with diverse sets of starting and ending points. The results have been compared among a total of 11 algorithms. The comparison of results shows that the proposed path planning methods generally outperform various algorithms, namely BPIB-RRT*, tGSRT, GWO, I-GWO, Ex-GWO, PSO, Improved BA, and WOA. The simulation results are analysed in terms of optimal path costs, execution time, and convergence rate.en_US
dc.identifier.doi10.1007/s00521-021-06179-0
dc.identifier.endpage15599en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue22en_US
dc.identifier.scopus2-s2.0-85107487212en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage15569en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-021-06179-0
dc.identifier.urihttps://hdl.handle.net/20.500.12662/3508
dc.identifier.volume33en_US
dc.identifier.wosWOS:000659898800002en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofNeural Computing & Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectOptimal path planningen_US
dc.subjectUnmanned aerial vehicleen_US
dc.subjectAutonomous mobile roboten_US
dc.subjectMetaheuristic algorithmen_US
dc.titleAdapted-RRT: novel hybrid method to solve three-dimensional path planning problem using sampling and metaheuristic-based algorithmsen_US
dc.typeArticleen_US

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