Deep-Reinforcement-Learning-Based Motion Planning for a Wide Range of Robotic Structures

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dc.contributor.authorParák, Romancs
dc.contributor.authorKůdela, Jakubcs
dc.contributor.authorMatoušek, Radomilcs
dc.contributor.authorJuříček, Martincs
dc.coverage.issue6cs
dc.coverage.volume12cs
dc.date.accessioned2024-10-14T09:03:53Z
dc.date.available2024-10-14T09:03:53Z
dc.date.issued2024-06-05cs
dc.description.abstractThe use of robot manipulators in engineering applications and scientific research has significantly increased in recent years. This can be attributed to the rise of technologies such as autonomous robotics and physics-based simulation, along with the utilization of artificial intelligence techniques. The use of these technologies may be limited due to a focus on a specific type of robotic manipulator and a particular solved task, which can hinder modularity and reproducibility in future expansions. This paper presents a method for planning motion across a wide range of robotic structures using deep reinforcement learning (DRL) algorithms to solve the problem of reaching a static or random target within a pre-defined configuration space. The paper addresses the challenge of motion planning in environments under a variety of conditions, including environments with and without the presence of collision objects. It highlights the versatility and potential for future expansion through the integration of OpenAI Gym and the PyBullet physics-based simulator.en
dc.formattextcs
dc.format.extent116-cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationComputation. 2024, vol. 12, issue 6, p. 116-.en
dc.identifier.doi10.3390/computation12060116cs
dc.identifier.issn2079-3197cs
dc.identifier.orcid0000-0002-2715-7820cs
dc.identifier.orcid0000-0002-4372-2105cs
dc.identifier.orcid0000-0002-3142-0900cs
dc.identifier.orcid0000-0002-7943-8659cs
dc.identifier.other189243cs
dc.identifier.researcheridAEA-4340-2022cs
dc.identifier.researcheridP-7327-2018cs
dc.identifier.researcheridJ-3692-2015cs
dc.identifier.scopus57225925410cs
dc.identifier.scopus56769626500cs
dc.identifier.scopus56180904400cs
dc.identifier.urihttps://hdl.handle.net/11012/249511
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofComputationcs
dc.relation.urihttps://www.mdpi.com/2079-3197/12/6/116cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2079-3197/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectdeep reinforcement learningen
dc.subjectmotion planningen
dc.subjectcollision avoidanceen
dc.subjectphysics-based simulationen
dc.subjectindustrial roboticsen
dc.titleDeep-Reinforcement-Learning-Based Motion Planning for a Wide Range of Robotic Structuresen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-189243en
sync.item.dbtypeVAVen
sync.item.insts2024.10.14 11:03:53en
sync.item.modts2024.09.19 15:32:04en
thesis.grantorVysoké učení technické v Brně. Fakulta strojního inženýrství. Ústav automatizace a informatikycs
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