Minimum Distance and Minimum Time Optimal Path Planning With Bioinspired Machine Learning Algorithms for Faulty Unmanned Air Vehicles

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dc.authoridAhmadi, Karim/0000-0002-2633-3351
dc.authoridTutsoy, Onder/0000-0001-6385-3025
dc.authoridasadi, davood/0000-0002-2066-6016
dc.contributor.authorTutsoy, Önder
dc.contributor.authorAhmadi, Karim
dc.contributor.authorAsadi, Davood
dc.contributor.authorNabavi-Chashmi, Seyed Yaser
dc.contributor.authorIqbal, Jamshed
dc.date.accessioned2025-01-06T17:44:07Z
dc.date.available2025-01-06T17:44:07Z
dc.date.issued2024
dc.description.abstractUnmanned air vehicles operate in highly dynamic and unknown environments where they can encounter unexpected and unseen failures. In the presence of emergencies, autonomous unmanned air vehicles should be able to land at a minimum distance or minimum time. Impaired unmanned air vehicles define actuator failures and this impairment changes their unstable and uncertain dynamics; henceforth, path planning algorithms must be adaptive and model-free. In addition, path planning optimization problems must consider the unavoidable actuator saturations, kinematic and dynamic constraints for successful real-time applications. Therefore, this paper develops 3D path planning algorithms for quadrotors with parametric uncertainties and various constraints. In this respect, this paper constructs a multi-dimensional particle swarm optimization and a multi-dimensional genetic algorithm to plan paths for translational, rotational, and Euler angles and generates the corresponding control signals. The algorithms are assessed and compared both in the simulation and experimental environments. Results show that the multi-dimensional genetic algorithm produces shorter minimum distance and minimum time paths under the constraints. The real-time experiments prove that the quadrotor exactly follows the produced path utilizing the available maximum rotor speeds.
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TBIdot;TAK)
dc.description.sponsorshipNo Statement Available
dc.identifier.doi10.1109/TITS.2024.3367769
dc.identifier.issn1524-9050
dc.identifier.issn1558-0016
dc.identifier.scopus2-s2.0-85188520903
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1109/TITS.2024.3367769
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2936
dc.identifier.wosWOS:001189441600001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIEEE-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Transactions on Intelligent Transportation Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectQuadrotors
dc.subjectPath planning
dc.subjectAutonomous aerial vehicles
dc.subjectMathematical models
dc.subjectTrajectory
dc.subjectHeuristic algorithms
dc.subjectUncertainty
dc.subjectActuator failures
dc.subjectimpaired quadrotors
dc.subjectmeta-heuristic algorithms
dc.subjectpath planning
dc.subjectunmanned air vehicles
dc.titleMinimum Distance and Minimum Time Optimal Path Planning With Bioinspired Machine Learning Algorithms for Faulty Unmanned Air Vehicles
dc.typeArticle

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