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dc.contributor.authorRomero-Garces, Adrian
dc.contributor.authorHidalgo-Paniagua, Alejandro
dc.contributor.authorGonzález-García, Martín 
dc.contributor.authorBandera-Rubio, Antonio Jesús 
dc.date.accessioned2024-02-08T09:52:05Z
dc.date.available2024-02-08T09:52:05Z
dc.date.issued2022-08
dc.identifier.citationRomero-Garcés, A.; Hidalgo-Paniagua, A.; González-García, M.; Bandera, A. On Managing Knowledge for MAPE-K Loops in Self-Adaptive Robotics Using a Graph-Based Runtime Model. Appl. Sci. 2022, 12, 8583. https://doi.org/10.3390/app12178583es_ES
dc.identifier.urihttps://hdl.handle.net/10630/30073
dc.description.abstractervice robotics involves the design of robots that work in a dynamic and very open environment, usually shared with people. In this scenario, it is very difficult for decision-making processes to be completely closed at design time, and it is necessary to define a certain variability that will be closed at runtime. MAPE-K (Monitor–Analyze–Plan–Execute over a shared Knowledge) loops are a very popular scheme to address this real-time self-adaptation. As stated in their own definition, they include monitoring, analysis, planning, and execution modules, which interact through a knowledge model. As the problems to be solved by the robot can be very complex, it may be necessary for several MAPE loops to coexist simultaneously in the robotic software architecture endowed in the robot. The loops will then need to be coordinated, for which they can use the knowledge model, a representation that will include information about the environment and the robot, but also about the actions being executed. This paper describes the use of a graph-based representation, the Deep State Representation (DSR), as the knowledge component of the MAPE-K scheme applied in robotics. The DSR manages perceptions and actions, and allows for inter- and intra-coordination of MAPE-K loops. The graph is updated at runtime, representing symbolic and geometric information. The scheme has been successfully applied in a retail intralogistics scenario, where a pallet truck robot has to manage roll containers for satisfying requests from human pickers working in the warehouse.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectRoboticaes_ES
dc.subject.otherKnowledge representationes_ES
dc.subject.otherMAPE-K loopes_ES
dc.subject.otherRuntime modeles_ES
dc.titleOn managing knowledge for MAPE-K Loops in self-adaptive robotics using a graph-based runtime modeles_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroE.T.S.I. Telecomunicaciónes_ES
dc.identifier.doihttps://doi.org/10.3390/app12178583
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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