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dc.contributor.advisorMorán Cárdenas, Antonio Manuel
dc.contributor.authorGlöde, Isabella
dc.date.accessioned2021-03-29T20:39:40Z
dc.date.available2021-03-29T20:39:40Z
dc.date.created2021
dc.date.issued2021-03-29
dc.identifier.urihttp://hdl.handle.net/20.500.12404/18676
dc.description.abstractOver the last few years autonomous driving had an increasingly strong impact on the automotive industry. This created an increased need for artificial intelligence algo- rithms which allow for computers to make human-like decisions. However, a compro- mise between the computational power drawn by these algorithms and their subsequent performance must be found to fulfil production requirements. In this thesis incremental deep learning strategies are used for the control of a mobile robot such as a four wheel steering vehicle. This strategy is similar to the human approach of learning. In many small steps the vehicle learns to achieve a specific goal. The usage of incremental training leads to growing knowledge-base within the system. It also provides the opportunity to use older training achievements to improve the system, when more training data is available. To demonstrate the capabilities of such an algorithm, two different models have been formulated. First, a more simple model with counter wheel steering, and second, a more complex, nonlinear model with independent steering. These two models are trained incrementally to follow different types of trajectories. Therefore an algorithm was established to generate useful initial points. The incremental steps allow the robot to be positioned further and further away from the desired trajectory in the environ- ment. Afterwards, the effects of different trajectory types on model behaviour are investigated by over one thousand simulation runs. To do this, path planning for straight lines and circles are introduced. This work demonstrates that even simulations with simple network structures can have high performance.es_ES
dc.description.uriTesises_ES
dc.language.isoenges_ES
dc.publisherPontificia Universidad Católica del Perúes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/2.5/pe/*
dc.subjectControl automático--Robots móvileses_ES
dc.subjectAprendizaje profundoes_ES
dc.subjectRedes neuronaleses_ES
dc.titleAutonomous control of a mobile robot with incremental deep learning neural networkses_ES
dc.typeinfo:eu-repo/semantics/masterThesises_ES
thesis.degree.nameMaestro en Ingeniería de Control y Automatizaciónes_ES
thesis.degree.levelMaestríaes_ES
thesis.degree.grantorPontificia Universidad Católica del Perú. Escuela de Posgradoes_ES
thesis.degree.disciplineIngeniería de Control y Automatizaciónes_ES
renati.advisor.dni10573987
renati.advisor.orcidhttps://orcid.org/0000-0001-9059-1446es_ES
renati.author.pasaporteCHLR78009
renati.discipline712037es_ES
renati.jurorReger, Johann
renati.jurorMorán Cárdenas, Antonio Manuel
renati.jurorEnciso Salas, Luis Miguel
renati.levelhttps://purl.org/pe-repo/renati/level#maestroes_ES
renati.typehttp://purl.org/pe-repo/renati/type#tesises_ES
dc.publisher.countryPEes_ES
dc.subject.ocdehttp://purl.org/pe-repo/ocde/ford#2.02.03es_ES


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