Diseño de un sistema automático de evasión de obstáculos basado en visión artificial para el robot colaborativo UR3

Los robots colaborativos están fabricados para realizar cada vez más tareas con los humanos, por esto es más seguro que un robot perciba su entorno para poder hacer movimientos que no comprometan la integridad tanto del humano como del robot. Aquí se muestra el desarrollo y validación de un sistema...

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Autores:
Blanco Vacca, Naifer David
Buitrago Rangel, Alex Julian
Tipo de recurso:
Trabajo de grado de pregrado
Fecha de publicación:
2022
Institución:
Universidad Autónoma de Bucaramanga - UNAB
Repositorio:
Repositorio UNAB
Idioma:
spa
OAI Identifier:
oai:repository.unab.edu.co:20.500.12749/18419
Acceso en línea:
http://hdl.handle.net/20.500.12749/18419
Palabra clave:
Mechatronic
Robotics
Algorithm
Matlab
Handlers
Automatic machinery
Artificial vision
Automation
Automatic control
Numerical analysis
Mecatrónica
Robot
Manipuladores
Maquinaria automática
Automatización
Control automático
Análisis numérico
Robótica
Algoritmo
Visión artificial
Rights
License
http://creativecommons.org/licenses/by-nc-nd/2.5/co/
id UNAB2_6d3a17d1e141cc942144100bde681657
oai_identifier_str oai:repository.unab.edu.co:20.500.12749/18419
network_acronym_str UNAB2
network_name_str Repositorio UNAB
repository_id_str
dc.title.spa.fl_str_mv Diseño de un sistema automático de evasión de obstáculos basado en visión artificial para el robot colaborativo UR3
dc.title.translated.spa.fl_str_mv Design of an automatic obstacle avoidance system based on artificial vision for the collaborative robot UR3
title Diseño de un sistema automático de evasión de obstáculos basado en visión artificial para el robot colaborativo UR3
spellingShingle Diseño de un sistema automático de evasión de obstáculos basado en visión artificial para el robot colaborativo UR3
Mechatronic
Robotics
Algorithm
Matlab
Handlers
Automatic machinery
Artificial vision
Automation
Automatic control
Numerical analysis
Mecatrónica
Robot
Manipuladores
Maquinaria automática
Automatización
Control automático
Análisis numérico
Robótica
Algoritmo
Visión artificial
title_short Diseño de un sistema automático de evasión de obstáculos basado en visión artificial para el robot colaborativo UR3
title_full Diseño de un sistema automático de evasión de obstáculos basado en visión artificial para el robot colaborativo UR3
title_fullStr Diseño de un sistema automático de evasión de obstáculos basado en visión artificial para el robot colaborativo UR3
title_full_unstemmed Diseño de un sistema automático de evasión de obstáculos basado en visión artificial para el robot colaborativo UR3
title_sort Diseño de un sistema automático de evasión de obstáculos basado en visión artificial para el robot colaborativo UR3
dc.creator.fl_str_mv Blanco Vacca, Naifer David
Buitrago Rangel, Alex Julian
dc.contributor.advisor.none.fl_str_mv González Acevedo, Hernando
Arizmendi Pereira, Carlos Julio
dc.contributor.author.none.fl_str_mv Blanco Vacca, Naifer David
Buitrago Rangel, Alex Julian
dc.contributor.cvlac.spa.fl_str_mv González Acevedo, Hernando [0000544655]
Arizmendi Pereira, Carlos Julio [0001381550]
dc.contributor.googlescholar.spa.fl_str_mv González Acevedo, Hernando [V8tga0cAAAAJ]
Arizmendi Pereira, Carlos Julio [JgT_je0AAAAJ]
dc.contributor.orcid.spa.fl_str_mv González Acevedo, Hernando [0000-0001-6242-3939]
dc.contributor.scopus.spa.fl_str_mv González Acevedo, Hernando [55821231500]
Arizmendi Pereira, Carlos Julio [16174088500]
dc.contributor.researchgate.spa.fl_str_mv González Acevedo, Hernando [Hernando_Gonzalez3]
Arizmendi Pereira, Carlos Julio [Carlos_Arizmendi2]
dc.subject.keywords.spa.fl_str_mv Mechatronic
Robotics
Algorithm
Matlab
Handlers
Automatic machinery
Artificial vision
Automation
Automatic control
Numerical analysis
topic Mechatronic
Robotics
Algorithm
Matlab
Handlers
Automatic machinery
Artificial vision
Automation
Automatic control
Numerical analysis
Mecatrónica
Robot
Manipuladores
Maquinaria automática
Automatización
Control automático
Análisis numérico
Robótica
Algoritmo
Visión artificial
dc.subject.lemb.spa.fl_str_mv Mecatrónica
Robot
Manipuladores
Maquinaria automática
Automatización
Control automático
Análisis numérico
dc.subject.proposal.spa.fl_str_mv Robótica
Algoritmo
Visión artificial
description Los robots colaborativos están fabricados para realizar cada vez más tareas con los humanos, por esto es más seguro que un robot perciba su entorno para poder hacer movimientos que no comprometan la integridad tanto del humano como del robot. Aquí se muestra el desarrollo y validación de un sistema de evasión de obstáculos basados en visión artificial implementado en el robot colaborativo UR3. Se implementa un algoritmo de visión artificial para que el robot pueda tener la capacidad de identificar los obstáculos que hay entre un punto inicial y uno final. Posteriormente se implementa un algoritmo de planeación de trayectorias el cual permite al robot saber cuál es la ruta que debe seguir para llegar del punto inicial al punto final sin colisionar con los obstáculos o consigo mismo. Ambos algoritmos se desarrollaron en el software MATLAB.
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2022-11-21T21:21:10Z
dc.date.available.none.fl_str_mv 2022-11-21T21:21:10Z
dc.date.issued.none.fl_str_mv 2022-08-20
dc.type.driver.none.fl_str_mv info:eu-repo/semantics/bachelorThesis
dc.type.local.spa.fl_str_mv Trabajo de Grado
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dc.identifier.reponame.spa.fl_str_mv reponame:Repositorio Institucional UNAB
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dc.relation.references.spa.fl_str_mv E. B. Kumar and V. Thiagarasu, "Color channel extraction in RGB images for segmentation," 2017 2nd International Conference on Communication and Electronics Systems (ICCES), 2017, pp. 234-239, doi: 10.1109/CESYS.2017.8321272.
M. Minos-Stensrud, O. H. Haakstad, O. Sakseid, B. Westby and A. Alcocer, "Towards Automated 3D reconstruction in SME factories and Digital Twin Model generation," 2018 18th International Conference on Control, Automation and Systems (ICCAS), 2018, pp. 1777-1781
Z. Shan, X. Xu, Y. Tao and H. Xiong, "A Trajectory Planning and Simulation Method for Welding Robot," 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), 2017, pp. 510-515, doi: 10.1109/CYBER.2017.8446181.
E. Shelhamer, J. Long and T. Darrell, "Fully Convolutional Networks for Semantic Segmentation," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 4, pp. 640-651, 1 April 2017, doi: 10.1109/TPAMI.2016.2572683.
K. He, X. Zhang, S. Ren and J. Sun, "Deep Residual Learning for Image Recognition," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770- 778, doi: 10.1109/CVPR.2016.90.
C. Lin and M. Li, "Motion planning with obstacle avoidance of an UR3 robot using charge system search," 2018 18th International Conference on Control, Automation and Systems (ICCAS), 2018, pp. 746-750.
A. Y. Lee, G. Jang and Y. Choi, "Infinitely differentiable and continuous trajectory planning for mobile robot control," 2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), 2013, pp. 357-361, doi: 10.1109/URAI.2013.6677386.
L. S. Scimmi, M. Melchiorre, S. Mauro and S. P. Pastorelli, "Implementing a Vision Based Collision Avoidance Algorithm on a UR3 Robot," 2019 23rd International Conference on Mechatronics Technology (ICMT), 2019, pp. 1-6, doi: 10.1109/ICMECT.2019.8932105.
L. S. Scimmi, M. Melchiorre, S. Mauro and S. Pastorelli, "Experimental Real-Time Setup for Vision Driven Hand-Over with a Collaborative Robot," 2019 International Conference on Control, Automation and Diagnosis (ICCAD), 2019, pp. 1-5, doi: 10.1109/ICCAD46983.2019.9037961.
Intel RealSense D400 Series Product Family [En linea]. Avalaible: https://www.intel.com/content/dam/support/us/en/documents/emerging technologies/intel-realsense-technology/Intel-RealSense-D400-Series-Datasheet.pdf [Último accedo: 2022]
Kinect for Windows SDK 2.0. http://www.todokinect.com/
L. Egorova and A. Lavrov, "Determination of workspace for motion capture using Kinect," 2015 56th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), 2015, pp. 1-4, doi: 10.1109/RTUCON.2015.7343155
Ibañez, R., Soria, Á., Teyseyre, A., & Campo, M. (2014). Easy gesture recognition for Kinect. Advances in Engineering Software, 76, 171–180. doi:10.1016/j.advengsoft.2014
M. Shoryabi, A. Foroutannia and A. Rowhanimanesh, "A 3D Deep Learning Approach for Classification of Gait Abnormalities Using Microsoft Kinect V2 Sensor," 2021 26th International Computer Conference, Computer Society of Iran (CSICC), 2021, pp. 1-4, doi: 10.1109/CSICC52343.2021.9420611.
Dive into depp learning, Residual Networks (ResNet). https://classic.d2l.ai/chapter_convolutional-modern/resnet.html#residual-networks resnet
MathWorks, Segmentación semántica. Mathworks. https://la.mathworks.com/solutions/image-video-processing/semantic segmentation.html.
"Universal Robot UR3". Universal Robots
Romero C. Juan, Paez R. David, Guarnizo M. José (2021). “UR3 Modelo Cinemático Inverso”
M. Ortiz-Salazar, A. Rodríguez-Liñán, L. M. Torres-Treviño and I. López-Juárez, "IMU Based Trajectory Generation and Modelling of 6-DOF Robot Manipulators," 2015 International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE), 2015, pp. 181-186, doi: 10.1109/ICMEAE.2015.27.
J. -D. Sun, G. -Z. Cao, W. -B. Li, Y. -X. Liang and S. -D. Huang, "Analytical inverse kinematic solution using the D-H method for a 6-DOF robot," 2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), 2017, pp. 714-716, doi: 10.1109/URAI.2017.7992807.
Y. Ren, H. Sun, Y. Tang and S. Wang, "Vision Based Object Grasping of Robotic Manipulator," 2018 24th International Conference on Automation and Computing (ICAC), 2018, pp. 1-5, doi: 10.23919/IConAC.2018.8749001.
L. D. Hanh and C. -Y. Lin, "Combining stereo vision and fuzzy image based visual servoing for autonomous object grasping using a 6-DOF manipulator," 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2012, pp. 1703-1708, doi: 10.1109/ROBIO.2012.6491213.
MathWorks, bidirectional rapidly exploring random trees. Mathworks. https://la.mathworks.com/help/robotics/ref/manipulator_rrt_true.png
T. LI and H. ZHU, "Research on model control of binocular robot vision system," 2018 Chinese Automation Congress (CAC), 2018, pp. 1794-1797, doi: 10.1109/CAC.2018.8623756
Iglesias García, M., & Lorenzo Prada, A.Sistema de Visión Artificial (Ingeniería).Universidad Carlos III.
MathWorks, «Computer Vision Toolbox,» [En línea]. Available: https://la.mathworks.com/products/computer-vision.html. [Último acceso: 2022]
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dc.publisher.faculty.spa.fl_str_mv Facultad Ingeniería
dc.publisher.program.spa.fl_str_mv Pregrado Ingeniería Mecatrónica
institution Universidad Autónoma de Bucaramanga - UNAB
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spelling González Acevedo, Hernando490b15a6-3d80-4525-a9a0-44e34b8f0937Arizmendi Pereira, Carlos Julio79e0125f-b191-4144-999b-281177ddaaf9Blanco Vacca, Naifer David2cb8db62-cb4a-4c96-b88d-e0e34a4674eaBuitrago Rangel, Alex Julian36ae2b52-b3c6-4a85-9ee1-6ce21404945eGonzález Acevedo, Hernando [0000544655]Arizmendi Pereira, Carlos Julio [0001381550]González Acevedo, Hernando [V8tga0cAAAAJ]Arizmendi Pereira, Carlos Julio [JgT_je0AAAAJ]González Acevedo, Hernando [0000-0001-6242-3939]González Acevedo, Hernando [55821231500]Arizmendi Pereira, Carlos Julio [16174088500]González Acevedo, Hernando [Hernando_Gonzalez3]Arizmendi Pereira, Carlos Julio [Carlos_Arizmendi2]Bucaramanga (Santander, Colombia)20222022-11-21T21:21:10Z2022-11-21T21:21:10Z2022-08-20http://hdl.handle.net/20.500.12749/18419instname:Universidad Autónoma de Bucaramanga - UNABreponame:Repositorio Institucional UNABrepourl:https://repository.unab.edu.coLos robots colaborativos están fabricados para realizar cada vez más tareas con los humanos, por esto es más seguro que un robot perciba su entorno para poder hacer movimientos que no comprometan la integridad tanto del humano como del robot. Aquí se muestra el desarrollo y validación de un sistema de evasión de obstáculos basados en visión artificial implementado en el robot colaborativo UR3. Se implementa un algoritmo de visión artificial para que el robot pueda tener la capacidad de identificar los obstáculos que hay entre un punto inicial y uno final. Posteriormente se implementa un algoritmo de planeación de trayectorias el cual permite al robot saber cuál es la ruta que debe seguir para llegar del punto inicial al punto final sin colisionar con los obstáculos o consigo mismo. Ambos algoritmos se desarrollaron en el software MATLAB.1. INTRODUCCIÓN 1. OBJETIVOS 1.1. Objetivo General 1.2. Objetivos específicos 2. ESTADO DEL ARTE 3. VISIÓN ARTIFICIAL 3.1. Software 3.2. Hardware 3.3. Calibración de Kinect V2 3.4. Reconocimiento basado en RBG-D 3.5. Redes neuronales convolucionales 3.6. Residual Networks 3.7. Segmentación semántica 3.8. Implementación del algoritmo basado en segmentación semántica 4. PLANEACIÓN DE TRAYECTORIAS 4.1. Robot UR3 4.2. Cinemática 4.3. Planeación de trayectorias 4.4. Protocolo de comunicación 5. VALIDACIONES 6. CONCLUSIONES 7. BIBLIOGRAFÍA 8. ANEXOS 8.1. Anexo 1 8.2. Anexo 2PregradoCollaborative robots are made to perform more and more tasks with humans, which is why it is safer for a robot to perceive its environment in order to make movements that do not compromise the integrity of both the human and the robot. Here we show the development and validation of an obstacle avoidance system based on artificial vision implemented in the UR3 collaborative robot. An artificial vision algorithm is implemented so that the robot can have the capacity to identify the obstacles that exist between an initial point and an end point. Subsequently, a trajectory planning algorithm which allows the robot to know the route it must follow to get from the start point to the end point without colliding with obstacles or with itself. Both algorithms were developed in MATLAB software.application/pdfspahttp://creativecommons.org/licenses/by-nc-nd/2.5/co/Abierto (Texto Completo)Atribución-NoComercial-SinDerivadas 2.5 Colombiahttp://purl.org/coar/access_right/c_abf2Diseño de un sistema automático de evasión de obstáculos basado en visión artificial para el robot colaborativo UR3Design of an automatic obstacle avoidance system based on artificial vision for the collaborative robot UR3Ingeniero MecatrónicoUniversidad Autónoma de Bucaramanga UNABFacultad IngenieríaPregrado Ingeniería Mecatrónicainfo:eu-repo/semantics/bachelorThesisTrabajo de Gradohttp://purl.org/coar/resource_type/c_7a1finfo:eu-repo/semantics/acceptedVersionhttp://purl.org/redcol/resource_type/TPMechatronicRoboticsAlgorithmMatlabHandlersAutomatic machineryArtificial visionAutomationAutomatic controlNumerical analysisMecatrónicaRobotManipuladoresMaquinaria automáticaAutomatizaciónControl automáticoAnálisis numéricoRobóticaAlgoritmoVisión artificialE. B. Kumar and V. Thiagarasu, "Color channel extraction in RGB images for segmentation," 2017 2nd International Conference on Communication and Electronics Systems (ICCES), 2017, pp. 234-239, doi: 10.1109/CESYS.2017.8321272.M. Minos-Stensrud, O. H. Haakstad, O. Sakseid, B. Westby and A. Alcocer, "Towards Automated 3D reconstruction in SME factories and Digital Twin Model generation," 2018 18th International Conference on Control, Automation and Systems (ICCAS), 2018, pp. 1777-1781Z. Shan, X. Xu, Y. Tao and H. Xiong, "A Trajectory Planning and Simulation Method for Welding Robot," 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), 2017, pp. 510-515, doi: 10.1109/CYBER.2017.8446181.E. Shelhamer, J. Long and T. Darrell, "Fully Convolutional Networks for Semantic Segmentation," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 4, pp. 640-651, 1 April 2017, doi: 10.1109/TPAMI.2016.2572683.K. He, X. Zhang, S. Ren and J. Sun, "Deep Residual Learning for Image Recognition," 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770- 778, doi: 10.1109/CVPR.2016.90.C. Lin and M. Li, "Motion planning with obstacle avoidance of an UR3 robot using charge system search," 2018 18th International Conference on Control, Automation and Systems (ICCAS), 2018, pp. 746-750.A. Y. Lee, G. Jang and Y. Choi, "Infinitely differentiable and continuous trajectory planning for mobile robot control," 2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), 2013, pp. 357-361, doi: 10.1109/URAI.2013.6677386.L. S. Scimmi, M. Melchiorre, S. Mauro and S. P. Pastorelli, "Implementing a Vision Based Collision Avoidance Algorithm on a UR3 Robot," 2019 23rd International Conference on Mechatronics Technology (ICMT), 2019, pp. 1-6, doi: 10.1109/ICMECT.2019.8932105.L. S. Scimmi, M. Melchiorre, S. Mauro and S. Pastorelli, "Experimental Real-Time Setup for Vision Driven Hand-Over with a Collaborative Robot," 2019 International Conference on Control, Automation and Diagnosis (ICCAD), 2019, pp. 1-5, doi: 10.1109/ICCAD46983.2019.9037961.Intel RealSense D400 Series Product Family [En linea]. Avalaible: https://www.intel.com/content/dam/support/us/en/documents/emerging technologies/intel-realsense-technology/Intel-RealSense-D400-Series-Datasheet.pdf [Último accedo: 2022]Kinect for Windows SDK 2.0. http://www.todokinect.com/L. Egorova and A. Lavrov, "Determination of workspace for motion capture using Kinect," 2015 56th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), 2015, pp. 1-4, doi: 10.1109/RTUCON.2015.7343155Ibañez, R., Soria, Á., Teyseyre, A., & Campo, M. (2014). Easy gesture recognition for Kinect. Advances in Engineering Software, 76, 171–180. doi:10.1016/j.advengsoft.2014M. Shoryabi, A. Foroutannia and A. Rowhanimanesh, "A 3D Deep Learning Approach for Classification of Gait Abnormalities Using Microsoft Kinect V2 Sensor," 2021 26th International Computer Conference, Computer Society of Iran (CSICC), 2021, pp. 1-4, doi: 10.1109/CSICC52343.2021.9420611.Dive into depp learning, Residual Networks (ResNet). https://classic.d2l.ai/chapter_convolutional-modern/resnet.html#residual-networks resnetMathWorks, Segmentación semántica. Mathworks. https://la.mathworks.com/solutions/image-video-processing/semantic segmentation.html."Universal Robot UR3". Universal RobotsRomero C. Juan, Paez R. David, Guarnizo M. José (2021). “UR3 Modelo Cinemático Inverso”M. Ortiz-Salazar, A. Rodríguez-Liñán, L. M. Torres-Treviño and I. López-Juárez, "IMU Based Trajectory Generation and Modelling of 6-DOF Robot Manipulators," 2015 International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE), 2015, pp. 181-186, doi: 10.1109/ICMEAE.2015.27.J. -D. Sun, G. -Z. Cao, W. -B. Li, Y. -X. Liang and S. -D. Huang, "Analytical inverse kinematic solution using the D-H method for a 6-DOF robot," 2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), 2017, pp. 714-716, doi: 10.1109/URAI.2017.7992807.Y. Ren, H. Sun, Y. Tang and S. Wang, "Vision Based Object Grasping of Robotic Manipulator," 2018 24th International Conference on Automation and Computing (ICAC), 2018, pp. 1-5, doi: 10.23919/IConAC.2018.8749001.L. D. Hanh and C. -Y. Lin, "Combining stereo vision and fuzzy image based visual servoing for autonomous object grasping using a 6-DOF manipulator," 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2012, pp. 1703-1708, doi: 10.1109/ROBIO.2012.6491213.MathWorks, bidirectional rapidly exploring random trees. Mathworks. https://la.mathworks.com/help/robotics/ref/manipulator_rrt_true.pngT. LI and H. ZHU, "Research on model control of binocular robot vision system," 2018 Chinese Automation Congress (CAC), 2018, pp. 1794-1797, doi: 10.1109/CAC.2018.8623756Iglesias García, M., & Lorenzo Prada, A.Sistema de Visión Artificial (Ingeniería).Universidad Carlos III.MathWorks, «Computer Vision Toolbox,» [En línea]. Available: https://la.mathworks.com/products/computer-vision.html. 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