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Ontological framework to improve motion planning of manipulative agents through semantic knowledge-based reasoning

dc.contributor.authorBernardo, Rodrigo
dc.contributor.authorSousa, João M.C.
dc.contributor.authorGonçalves, Paulo
dc.date.accessioned2023-12-22T12:42:39Z
dc.date.available2023-12-22T12:42:39Z
dc.date.issued2023
dc.description.abstractThis paper describes the actions taken in developing a framework that aims to improve the motion planning of a manipulative robotic agent through reasoning based on semantic knowledge. The Semantic Web Rule Language (SWRL) was employed to draw new insights from the existing information about the robotic system and its environment. Recent ontology-based standards have been developed (IEEE 1872-2015; IEEE 1872.2-2021; IEEE 7007-2021), and others are currently under development (IEEE P1872.1; IEEE P1872.3) to improve robot performance in task execution. Ontological knowledge “semantic map" was generated using a deep neural network trained to detect and classify objects in the environment where the manipulator agent acts. Manipulation constraints were deduced, and the environment corresponding to the agent’s manipulation workspace was created so the planner could interpret it to generate a collision-free path. Several SPARQL queries were used to explore the semantic map and allow ontological reasoning. The proposed framework was implemented and validated in a real experimental setting, using the ROSPlan planning framework to perform the planning tasks. This ontology-based framework proved to be a promising strategy. E.g., it allows the robotic manipulative agent to interact with objects, e.g., to choose a mobile phone or a water bottle, using semantic information from the environment to solve the requested tasks.pt_PT
dc.description.sponsorshipThis work is financed by national funds through FCT - Foundation for Science and Technology, I.P., through IDMEC, under LAETA, project UIDB/50022/2020. The work of Rodrigo Bernardo was supported by the PhD Scholarship BD/6841/2020 from FCT. This work has received funding from: the project 0770_EUROAGE2_4_E (POCTEP Programa Interreg V-A Spain-Portugal), and the European Union’s Horizon 2020 programme under StandICT.eu 2023 (under Grant Agreement No.: 951972).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationBERNARDO, Rodrigo ; SOUSA, J.M.C. ; GONÇALVES; P.J.S. (2023) - Ontological framework to improve motion planning of manipulative agents through semantic knowledge-based reasoning. In RobOntics 2023: Workshop on Ontologies in Autonomous Robotic, Seoul. CEUR WS. Vol. 3595.pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.11/8754
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherCEURpt_PT
dc.relationFCT - Foundation for Science and Technology, I.P., through IDMEC, under LAETA, project UIDB/50022/2020pt_PT
dc.relationPhD Scholarship BD/6841/2020 from FCTpt_PT
dc.relationproject 0770_EUROAGE2_4_E (POCTEP Programa Interreg V-A Spain-Portugal)pt_PT
dc.relationEuropean Union’s Horizon 2020 programme under StandICT.eu 2023 (under Grant Agreement No.: 951972)pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectKnowledge representationpt_PT
dc.subjectOntologiespt_PT
dc.subjectManipulationpt_PT
dc.subjectMotion planningpt_PT
dc.subjectSemantic mapspt_PT
dc.titleOntological framework to improve motion planning of manipulative agents through semantic knowledge-based reasoningpt_PT
dc.typebook part
dspace.entity.typePublication
oaire.citation.conferencePlaceSeul, Coreia do Sulpt_PT
oaire.citation.titleRobOntics 2023pt_PT
oaire.citation.volume3595pt_PT
person.familyNameGonçalves
person.givenNamePaulo
person.identifier.ciencia-id2816-A2FA-C5A3
person.identifier.orcid0000-0002-8692-7338
person.identifier.ridE-5640-2012
person.identifier.scopus-author-id35853838000
rcaap.rightsopenAccesspt_PT
rcaap.typebookPartpt_PT
relation.isAuthorOfPublication86a6a234-d690-4c2b-8bee-d58005eebba2
relation.isAuthorOfPublication.latestForDiscovery86a6a234-d690-4c2b-8bee-d58005eebba2

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