Synthetic data-augmented learning pipelines for cobotic packing work cells

dc.contributorÁlvarez Martínez, David
dc.contributorTabares Pozos, Alejandra
dc.contributorCentro para la Optimización y Probabilidad Aplicada
dc.contributorProducción y Logística
dc.creatorMartínez Franco, Juan Camilo
dc.date.accessioned2023-07-21T14:25:45Z
dc.date.accessioned2023-09-07T01:13:05Z
dc.date.available2023-07-21T14:25:45Z
dc.date.available2023-09-07T01:13:05Z
dc.date.created2023-07-21T14:25:45Z
dc.date.issued2023-06-08
dc.identifierhttp://hdl.handle.net/1992/68615
dc.identifierinstname:Universidad de los Andes
dc.identifierreponame:Repositorio Institucional Séneca
dc.identifierrepourl:https://repositorio.uniandes.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8728177
dc.description.abstractSafe human-robot interaction has consistently been one of the main concerns behind industrial robot applications. This is particularly true with the emerging trends in collaborative robotics and their use in quick, relatively inexpensive automation of warehousing and distribution tasks. As such, there is an increasing need for safety features in response to dynamic workspace conditions that were not present in industrial environments in the past. This thesis aims to introduce novel methodologies that allow for the generation of dynamically stable packing pattens, more accurate, comprehensive understanding of 3D scenes from data captured with RGB-D sensors, as well as more energy-efficient and collision free trajectories in collaborative manipulators. The first contribution is based on dynamic stability studies of cutting and packing problems, the next contribution is focused on a new procedure for hand-eye calibration that is not dependent on printed grid patterns. The next addition to the state of the art is related to domain randomization, where approaches towards synthetic data generation and training procedures are proposed. Lastly, a reinforcement learning scheme making use of proximal policy optimization and engineered rewards aiming to reduce inefficient movements in collision avoidant path planning is presented. The mentioned contributions were implemented via a case study in an automated packing operation.
dc.languageeng
dc.publisherUniversidad de los Andes
dc.publisherDoctorado en Ingeniería
dc.publisherFacultad de Ingeniería
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dc.rightsAtribución 4.0 Internacional
dc.rightsAtribución 4.0 Internacional
dc.rightshttp://creativecommons.org/licenses/by/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.titleSynthetic data-augmented learning pipelines for cobotic packing work cells
dc.typeTrabajo de grado - Doctorado


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