Robotic Collaborative workstation based on Object 6DOF Pose Estimation

Industrial manufacturing assembly lines are characterized by performing manual tasks that lead operators to outstanding workload, both physically and mentally, that end up in stress and injuries. This thesis delves into the implementation of an Industry 5.0 robotic collaborative workstation equipped...

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Detalles Bibliográficos
Autor: Vilanova Sánchez, José Ramón
Tipo de recurso: tesis de maestría
Fecha de publicación:2024
País:España
Institución:Universidad Loyola Andalucía
Repositorio:Brújula
OAI Identifier:oai:repositorio.uloyola.es:20.500.12412/6137
Acceso en línea:https://hdl.handle.net/20.500.12412/6137
Access Level:acceso abierto
Palabra clave:Instance Segmentation
Object Pose estimation
3D reconstruction
Robotics Path Planning
Computer Vision
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spelling Robotic Collaborative workstation based on Object 6DOF Pose EstimationVilanova Sánchez, José RamónInstance SegmentationObject Pose estimation3D reconstructionRobotics Path PlanningComputer VisionIndustrial manufacturing assembly lines are characterized by performing manual tasks that lead operators to outstanding workload, both physically and mentally, that end up in stress and injuries. This thesis delves into the implementation of an Industry 5.0 robotic collaborative workstation equipped with cutting-edge Artificial Intelligence (AI) systems for an effective human-robot interaction. The main functionality of this system is the implementation of computer vision algorithms to locate tools and perform robotics gripping. Foundation Pose is the system which provides the position and orientation of unseen objects to the robot, enabling an effective path planning and object gripping task. Accurate segmentation and 3D models are required for performing that pose estimation, employing and evaluating CNOS and SAM-6D systems for the generation of compatible instance segmentation masks, and BundleSDF for reconstructing 3D models of real-world objects. The final stage of this thesis includes the robotics path planning for object manipulation. This research compares the effectiveness of Reinforcement Learning PPO algorithm, contrasting it with traditional robotics methods using Moveit! Library, ending with object gripping. The results obtained are promising, reaching and picking the object successfully in 10 trajectories. This means a perfect synergy between Foundation Pose, CNOS (which resulted better for segmenting), and its transformation to robot coordinate system. This system is already part of CATEC’s pilot factory of the project 5R Network and offers encouraging results that could impact the aerospace industry. Despite further advancements are needed for real-world factory robustness, this research provides valuable insights for integrating this framework into actual aerospace manufacturing environments.Máster Universitario en Inteligencia ArtificialRus Pezzi, Joaquín2024info:eu-repo/semantics/masterThesishttps://hdl.handle.net/20.500.12412/6137reponame:Brújulainstname:Universidad Loyola AndalucíaIngléshttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:repositorio.uloyola.es:20.500.12412/61372026-06-24T12:48:37Z
dc.title.none.fl_str_mv Robotic Collaborative workstation based on Object 6DOF Pose Estimation
title Robotic Collaborative workstation based on Object 6DOF Pose Estimation
spellingShingle Robotic Collaborative workstation based on Object 6DOF Pose Estimation
Vilanova Sánchez, José Ramón
Instance Segmentation
Object Pose estimation
3D reconstruction
Robotics Path Planning
Computer Vision
title_short Robotic Collaborative workstation based on Object 6DOF Pose Estimation
title_full Robotic Collaborative workstation based on Object 6DOF Pose Estimation
title_fullStr Robotic Collaborative workstation based on Object 6DOF Pose Estimation
title_full_unstemmed Robotic Collaborative workstation based on Object 6DOF Pose Estimation
title_sort Robotic Collaborative workstation based on Object 6DOF Pose Estimation
dc.creator.none.fl_str_mv Vilanova Sánchez, José Ramón
author Vilanova Sánchez, José Ramón
author_facet Vilanova Sánchez, José Ramón
author_role author
dc.contributor.none.fl_str_mv Rus Pezzi, Joaquín
dc.subject.none.fl_str_mv Instance Segmentation
Object Pose estimation
3D reconstruction
Robotics Path Planning
Computer Vision
topic Instance Segmentation
Object Pose estimation
3D reconstruction
Robotics Path Planning
Computer Vision
description Industrial manufacturing assembly lines are characterized by performing manual tasks that lead operators to outstanding workload, both physically and mentally, that end up in stress and injuries. This thesis delves into the implementation of an Industry 5.0 robotic collaborative workstation equipped with cutting-edge Artificial Intelligence (AI) systems for an effective human-robot interaction. The main functionality of this system is the implementation of computer vision algorithms to locate tools and perform robotics gripping. Foundation Pose is the system which provides the position and orientation of unseen objects to the robot, enabling an effective path planning and object gripping task. Accurate segmentation and 3D models are required for performing that pose estimation, employing and evaluating CNOS and SAM-6D systems for the generation of compatible instance segmentation masks, and BundleSDF for reconstructing 3D models of real-world objects. The final stage of this thesis includes the robotics path planning for object manipulation. This research compares the effectiveness of Reinforcement Learning PPO algorithm, contrasting it with traditional robotics methods using Moveit! Library, ending with object gripping. The results obtained are promising, reaching and picking the object successfully in 10 trajectories. This means a perfect synergy between Foundation Pose, CNOS (which resulted better for segmenting), and its transformation to robot coordinate system. This system is already part of CATEC’s pilot factory of the project 5R Network and offers encouraging results that could impact the aerospace industry. Despite further advancements are needed for real-world factory robustness, this research provides valuable insights for integrating this framework into actual aerospace manufacturing environments.
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.12412/6137
url https://hdl.handle.net/20.500.12412/6137
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Brújula
instname:Universidad Loyola Andalucía
instname_str Universidad Loyola Andalucía
reponame_str Brújula
collection Brújula
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.81155

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