Diseño de un sistema que determine regiones de agarre de objetos cilíndricos en un entorno semi-estructurado basado en visión

ilustraciones, diagramas, fotografías

Autores:
Ochoa Morón, Daniel Francisco
Tipo de recurso:
Fecha de publicación:
2024
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/86555
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/86555
https://repositorio.unal.edu.co/
Palabra clave:
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
Agarre
Aprendizaje profundo
Manipulador
Visión de máquina
Grasping
Deep learning
Manipulator
Machine vision
Inteligencia artificial
Propiedad física
Artificial intelligence
Physical properties
visión artificial
computer vision
Rights
openAccess
License
Atribución-NoComercial-SinDerivadas 4.0 Internacional
id UNACIONAL2_d14d7fef4332b85c257f706741c6b73f
oai_identifier_str oai:repositorio.unal.edu.co:unal/86555
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv Diseño de un sistema que determine regiones de agarre de objetos cilíndricos en un entorno semi-estructurado basado en visión
dc.title.translated.eng.fl_str_mv Design of a system that determines grasping regions of cylindrical objects in a semi-structured vision-based environment
title Diseño de un sistema que determine regiones de agarre de objetos cilíndricos en un entorno semi-estructurado basado en visión
spellingShingle Diseño de un sistema que determine regiones de agarre de objetos cilíndricos en un entorno semi-estructurado basado en visión
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
Agarre
Aprendizaje profundo
Manipulador
Visión de máquina
Grasping
Deep learning
Manipulator
Machine vision
Inteligencia artificial
Propiedad física
Artificial intelligence
Physical properties
visión artificial
computer vision
title_short Diseño de un sistema que determine regiones de agarre de objetos cilíndricos en un entorno semi-estructurado basado en visión
title_full Diseño de un sistema que determine regiones de agarre de objetos cilíndricos en un entorno semi-estructurado basado en visión
title_fullStr Diseño de un sistema que determine regiones de agarre de objetos cilíndricos en un entorno semi-estructurado basado en visión
title_full_unstemmed Diseño de un sistema que determine regiones de agarre de objetos cilíndricos en un entorno semi-estructurado basado en visión
title_sort Diseño de un sistema que determine regiones de agarre de objetos cilíndricos en un entorno semi-estructurado basado en visión
dc.creator.fl_str_mv Ochoa Morón, Daniel Francisco
dc.contributor.advisor.spa.fl_str_mv Cárdenas Herrera, Pedro Fabián
Grisales Palacio, Victor Hugo
dc.contributor.author.spa.fl_str_mv Ochoa Morón, Daniel Francisco
dc.contributor.orcid.spa.fl_str_mv Ochoa Morón, Daniel [0000-0003-1042-4379]
dc.contributor.cvlac.spa.fl_str_mv Ochoa Morón, Daniel [1eHzLpAAAAAJ]
dc.subject.ddc.spa.fl_str_mv 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
topic 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
Agarre
Aprendizaje profundo
Manipulador
Visión de máquina
Grasping
Deep learning
Manipulator
Machine vision
Inteligencia artificial
Propiedad física
Artificial intelligence
Physical properties
visión artificial
computer vision
dc.subject.proposal.spa.fl_str_mv Agarre
Aprendizaje profundo
Manipulador
Visión de máquina
dc.subject.proposal.eng.fl_str_mv Grasping
Deep learning
Manipulator
Machine vision
dc.subject.unesco.spa.fl_str_mv Inteligencia artificial
Propiedad física
dc.subject.unesco.eng.fl_str_mv Artificial intelligence
Physical properties
dc.subject.wikidata.spa.fl_str_mv visión artificial
dc.subject.wikidata.eng.fl_str_mv computer vision
description ilustraciones, diagramas, fotografías
publishDate 2024
dc.date.accessioned.none.fl_str_mv 2024-07-18T14:28:59Z
dc.date.available.none.fl_str_mv 2024-07-18T14:28:59Z
dc.date.issued.none.fl_str_mv 2024-01-31
dc.type.spa.fl_str_mv Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TM
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/86555
dc.identifier.instname.spa.fl_str_mv Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv https://repositorio.unal.edu.co/
url https://repositorio.unal.edu.co/handle/unal/86555
https://repositorio.unal.edu.co/
identifier_str_mv Universidad Nacional de Colombia
Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv spa
language spa
dc.relation.references.spa.fl_str_mv Adarsh, Pranav ; Rathi, Pratibha ; Kumar, Manoj: YOLO v3-Tiny: Object Detection and Recognition using one stage improved model. En: 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS) (2020), 3, p. 687–694. ISBN 9781728151977
Amend, John R. ; Brown, Eric ; Rodenberg, Nicholas ; Jaeger, Heinrich M. ; Lipson, Hod: A positive pressure universal gripper based on the jamming of granular material. En: IEEE Transactions on Robotics 28 (2012), 4, Nr. 2, p. 341–350. – ISSN 15523098
Barequet, Gill ; Wolfers, Barbara: Optimizing a strip separating two polygons. En: Graphical Models and Image Processing 60 (1998), Nr. 3, p. 214–221. – ISSN 10773169
Bhattacharya, Binay ; Toussaint, Godfried: Computing shortest transversals. En: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 510 LNCS (1991), p. 649–660. – ISBN 9783540542339
Bhattacharya, Binay K. ; Toussaint, Godfried T.: Efficient algorithms for computing the maximum distance between two finite planar sets. En: Journal of Algorithms 4 (1983), 6, Nr. 2, p. 121–136. – ISSN 0196–6774
Bohg, J ; Morales, A ; Asfour, T: Data-driven grasp synthesis—a survey. En: IEEE Transactions on robotics 30 (2014), p. 289–309
Cebollada, S ; Payá L ; Flores, M ; . . . , A Peidró Expert Systems w. ; 2021, undefined: A state-of-the-art review on mobile robotics tasks using artificial intelligence and visual data. En: Expert Systems with Applications 167
Dai, Jifeng ; He, Kaiming ; Sun, Jian: Convolutional feature masking for joint object and stuff segmentation. En: Computer Vision and Pattern Recognition 07-12-June-2015 (2014), 10, p. 3992–4000. – ISBN 9781467369640
Deimel, Raphael ; Brock, Oliver: A novel type of compliant and underactuated robotic hand for dexterous grasping. En: https://doi.org/10.1177/0278364915592961 35 (2015), 8, Nr. 1-3, p. 161–185. – ISSN 17413176
Deng, Z ; Zheng, X ; Zhang, L ; Zhang, J: A learning framework for semantic reach-to-grasp tasks integrating machine learning and optimization. En: Robotics and Autonomous Systems 108 (2018), p. 140–152
Detry, Renaud ; Ek, Carl H. ; Madry, Marianna ; Piater, Justus ; Kragic, Danica: Generalizing grasps across partly similar objects. En: 2012 IEEE International Conference on Robotics and Automation, IEEE, 5 2012. – ISBN 978–1–4673–1405–3, p. 3791–3797
Domae, Yukiyasu ; Okuda, Haruhisa ; Taguchi, Yuichi ; Sumi, Kazuhiko ; Hirai, Takashi: Fast graspability evaluation on single depth maps for bin picking with general grippers. En: 2014 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 5 2014. – ISBN 978–1–4799–3685–4, p. 1997–2004
Eberly, David: Minimum-Area Rectangle Containing a Set of Points. En: Geometric Tools, LLC (2015)
Eizicovits, D ; Berman, S: Automatic graspability map generation based on shape-primitives for unknown and familiar objects. En: Robotics and Autonomous Systems (2018)
Erhan, Dumitru ; Szegedy, Christian ; Toshev, Alexander ; Anguelov, Dragomir: Scalable Object Detection using Deep Neural Networks. En: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2013), 12, p. 2155–2162. – ISBN 9781479951178
Farabet, Clement ; Couprie, Camille ; Najman, Laurent ; Lecun, Yann: Learning hierarchical features for scene labeling. En: IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (2013), Nr. 8, p. 1915–1929. – ISSN 01628828
Fogel, I ; Sagi, D: Biological Cybernetics Gabor Filters as Texture Discriminator. En: Biol. Cybern 61 (1989), p. 113
Girshick, Ross ; Donahue, Jeff ; Darrell, Trevor ; Malik, Jitendra: Region-based Convolutional Networks for Accurate Object Detection and Segmentation. En: IEEE transactions on pattern analysis and machine intelligence 38 (2015), p. 142–158
Goldfeder, C ; Ciocarlie, M ; Dang, H ; Allen, PK: The columbia grasp database. En: IEEE international conference on robotics and automation (2009), p. 1710–1716
Gouda, Anas ; Ghanem, Abraham ; Kaiser, Pascal ; Hompel, Michael T.: Object class-agnostic segmentation for practical CNN utilization in industry. En: 2021 6th International Conference on Mechanical Engineering and Robotics Research, ICMERR 2021 (2021), p. 97–105. ISBN 9781665406420
Gouda, Anas ; Roidl, Moritz: DoUnseen: Tuning-Free Class-Adaptive Object Detection of Unseen Objects for Robotic Grasping. En: arXiv preprint arXiv:2404.06277 (2023)
Houle, Michael E. ; Toussaint, Godfried T.: Computing the Width of a Set. En: IEEE Transactions on Pattern Analysis and Machine Intelligence 10 (1988), Nr. 5, p. 761–765. – ISSN 01628828
Hu, Xiao d. ; Wang, Xin q. ; Meng, Fan j. ; Hua, Xia ; Yan, Yu j. ; Li, Yu y. ; Huang, Jing ; Jiang, Xun l.: Gabor-CNN for object detection based on small samples. En: Defence Technology 16 (2020), 12, Nr. 6, p. 1116–1129. – ISSN 22149147
Hu, Zhongxu ; Tan, Runjia ; Zhou, Yanxin ; Woon, Junyang ; Lv, Chen: Template-Based Category-Agnostic Instance Detection for Robotic Manipulation. En: IEEE Robotics and Automation Letters 7 (2022), 10, Nr. 4, p. 12451–12458. – ISSN 23773766
Hughes, Josie ; Culha, Utku ; Giardina, Fabio ; Guenther, Fabian ; Rosendo, Andre; Iida, Fumiya: Soft manipulators and grippers: A review. En: Frontiers Robotics AI 3 (2016), 11, Nr. NOV, p. 223168. – ISSN 22969144
Ito, Seiji ; Yoshioka, Michifumi ; Omatu, Sigeru ; Kita, Kouji ; Kugo, Kouichi: An image segmentation method using histograms and the human characteristics of HSI color space for a scene image. En: Artificial Life and Robotics 2006 10:1 10 (2006), 7, Nr. 1, p. 6–10. – ISSN 1614–7456
Kasper, Alexander ; Xue, Zhixing ; Dillmann, R ̈udiger: The KIT object models database: An object model database for object recognition, localization and manipulation in service robotics. En: The International Journal of Robotics Research 31 (2012), 7, Nr. 8, p. 927–934. – ISSN 0278–3649
Kumra, Sulabh ; Kanan, Christopher: Robotic grasp detection using deep convolutional neural networks. En: IEEE International Conference on Intelligent Robots and Systems 2017 (2017), 12, p. 769 – 776. – ISBN 9781538626825
Lenz, Ian ; Lee, Honglak ; Saxena, Ashutosh: Deep learning for detecting robotic grasps. En: The International Journal of Robotics Research 34 (2015), 4, Nr. 4-5, p. 705–724. – ISSN 0278–3649
Li, Miao ; Hang, Kaiyu ; Kragic, Danica ; Billard, Aude: Dexterous grasping under shape uncertainty. En: Robotics and Autonomous Systems 75 (2016), 1, p. 352–364. – ISSN 0921–8890
Liao, Ping-Sung ; Chen, Tse-Sheng ; Chung, Pau-Choo: A Fast Algorithm for Multilevel Thresholding. En: JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 17 (2001), p. 713–727
Liu, Chunfang ; Sun, Fuchun ; Ban, Xiaojuan: An effective method for grasp planning on objects with complex geometry combining human experience and analytical approach. En: Science China Information Sciences 59 (2016), 11, Nr. 11, p. 112212. – ISSN 1674–733X
Lozano-P ́erez, Tom ́as: Spatial Planning: A Configuration Space Approach. En: IEEE Transactions on Computers C-32 (1983), Nr. 2, p. 108–120. – ISSN 00189340
McGlone, J. C. ; Mikhail, Edward M. ; Bethel, James S. Manual of Photogrammetry, 5th Edition - Dialnet. 1 2004
Mebatsion, H.K. ; Paliwal, J.: Machine vision based automatic separation of touching convex shaped objects. En: Computers in Industry 63 (2012), 9, Nr. 7, p. 723–730. – ISSN 0166–3615
Mercier, Jean P. ; Garon, Mathieu ; Giguere, Philippe ; Lalonde, Jean F.: Deep Template-based Object Instance Detection. En: Proceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021 (2019), 11, p. 1506–1515. ISBN 9780738142661
Mizushima, Kaori ; Oku, Takumi ; Suzuki, Yosuke ; Tsuji, Tokuo ; Watanabe, Tetsuyou: Multi-fingered robotic hand based on hybrid mechanism of tendon-driven and jamming transition. En: 2018 IEEE International Conference on Soft Robotics, RoboSoft 2018 (2018), 7, p. 376–381. ISBN 9781538645161
Otsu, Nobuyuki: THRESHOLD SELECTION METHOD FROM GRAY-LEVEL HISTOGRAMS. En: IEEE Trans Syst Man Cybern SMC-9 (1979), Nr. 1, p. 62–66. – ISSN 00189472
Park, Kiru ; Patten, Timothy ; Prankl, Johann ; Vincze, Markus: Multi-task template matching for object detection, segmentation and pose estimation using depth images. En: Proceedings - IEEE International Conference on Robotics and Automation 2019-May (2019), 5, p. 7207–7213. – ISBN 9781538660263
Patil, Rajesh V. ; Reddy, Y. P.: An Autonomous Technique for Multi Class Weld Imperfections Detection and Classification by Support Vector Machine. En: Journal of Nondestructive Evaluation 40 (2021), 9, Nr. 3. – ISSN 15734862
Pfeifer, Rolf ; Lungarella, Max ; Iida, Fumiya: The challenges ahead for bio-inspired ’soft’ robotics. En: Communications of the ACM 55 (2012), 11, Nr. 11, p. 76–87. – ISSN 00010782
Pirzadeh, Hormoz. ; Toussaint, Godfried T.: Computational geometry with the rotating calipers, McGill University, Tesis de Grado, 1999
Prattichizzo, Domenico ; Malvezzi, Monica ; Gabiccini, Marco ; Bicchi, Antonio: On the manipulability ellipsoids of underactuated robotic hands with compliance. En: Robotics and Autonomous Systems 60 (2012), 3, Nr. 3, p. 337–346. – ISSN 0921–8890
Ramli, S ; Mustafa, M M. ; Wahab, D A. ; Hussain, A: Plastic bottle shape classification using partial erosion-based approach. En: 2010 6th International Colloquium on Signal Processing & its Applications, IEEE, 5 2010. – ISBN 978–1–4244–7121–8, p. 1–4
Redmon, Joseph ; Angelova, Anelia: Real-time grasp detection using convolutional neural networks. En: 2015 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 5 2015. – ISBN 978–1–4799–6923–4, p. 1316–1322
Redmon, Joseph ; Farhadi, Ali: YOLO9000: Better, Faster, Stronger. En: Computer Vision and Pattern Recognition 2017-January (2016), 11, p. 6517–6525. ISBN 9781538604571
Rich, Steven I. ; Wood, Robert J. ; Majidi, Carmel: Untethered soft robotics. En: Nature Electronics 2018 1:2 1 (2018), 2, Nr. 2, p. 102–112. – ISSN 2520–1131
Roa, Máximo A. ; Suárez, Raúl: Grasp quality measures: review and performance. En: Autonomous Robots 38 (2015), 1, Nr. 1, p. 65–88. – ISSN 0929–5593
Robert, Jean M. ; Toussaint, Godfried T.: Linear approximation of simple objects. En: Computational Geometry: Theory and Applications 4 (1994), Nr. 1, p. 27–52. – ISSN 0925–7721
Sahbani, A ; El-Khoury, S ; Bidaud, P: An overview of 3D object grasp synthesis algorithms. En: Robotics and Autonomous Systems 60 (2012)
Sankur, Bulent: Survey over image thresholding techniques and quantitative performance evaluation. En: Journal of Electronic Imaging 13 (2004), 1, Nr. 1, p. 146. – ISSN 1017–9909
Saxena, A ; Driemeyer, J ; Ng, AY: Robotic grasping of novel objects using vision. En: The International Journal of Robotics Research 2 (2008), p. 157–163
Scavino, Edgar ; Wahab, Dzuraidah A. ; Hussain, Aini ; Basri, Hassan ; Mustafa, Mohd M.: Application of automated image analysis to the identification and extraction of recyclable plastic bottles. En: Journal of Zhejiang University-SCIENCE A 10 (2009), 6, Nr. 6, p. 794–799. – ISSN 1673–565X
Sui, Dongbao ; Zhu, Yanhe ; Zhao, Sikai ; Wang, Tianshuo ; Agrawal, Sunil K. ; Zhang, He ; Zhao, Jie: A Bioinspired Soft Swallowing Gripper for Universal Adaptable Grasping. En: Soft Robotics 9 (2022), 2, Nr. 1, p. 36–56. – ISSN 21695180
Tochon, G: Compression of color images.
Toussaint, Godfried T. ; Toussaint, Godfried T.: A Simple Linear Algorithm for Intersecting Convex Polygons. En: THE VISUAL COMPUTER 1 (1985), p. 118–123
Vahrenkamp, N ; Westkamp, L: Part-based grasp planning for familiar objects. En: 2016 IEEE-RAS 16th international conference on humanoid robots (humanoids) (2016), p. 919–925
Wang, Yong ; Liu, Zhi ; Zhang, Yun: Image based adaptive coordinated control for cooperative manipulators. En: 2016 Chinese Control and Decision Conference (CCDC), IEEE, 5 2016. – ISBN 978–1–4673–9714–8, p. 5383–5388
Wang, Zhaokun ; Peng, Binbin ; Huang, Yanjun ; Sun, Guanqun: Classification for plastic bottles recycling based on image recognition. En: Waste Management 88 (2019), 4, p. 170–181. – ISSN 0956–053X
Zhao, Gang ; Zou, Shuilong ; Wu, Huijie: Improved Algorithm for Face Mask Detection Based on YOLO-v4. En: International Journal of Computational Intelligence Systems 16 (2023), Nr. 3, p. 104. ISBN 0123456789
Zhu, Jie ; Yu, Jian ; Wang, Chaomurilige ; Li, Fan-Zhang: Object recognition via contextual color attention. En: Journal of Visual Communication and Image Representation 27 (2015), 2, p. 44–56. – ISSN 10473203
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dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
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dc.format.extent.spa.fl_str_mv viii, 89 páginas
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv Bogotá - Ingeniería - Maestría en Ingeniería - Automatización Industrial
dc.publisher.faculty.spa.fl_str_mv Facultad de Ingeniería
dc.publisher.place.spa.fl_str_mv Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv Universidad Nacional de Colombia - Sede Bogotá
institution Universidad Nacional de Colombia
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spelling Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Cárdenas Herrera, Pedro Fabiánf2a5d883628e057fb0a0370af163e714600Grisales Palacio, Victor Hugoeaa2fdd879cc6742a48d15ffebe10a00600Ochoa Morón, Daniel Franciscob6e951a09453d791f826e830090554df600Ochoa Morón, Daniel [0000-0003-1042-4379]Ochoa Morón, Daniel [1eHzLpAAAAAJ]2024-07-18T14:28:59Z2024-07-18T14:28:59Z2024-01-31https://repositorio.unal.edu.co/handle/unal/86555Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramas, fotografíasLa presente tesis de maestría se focaliza en el desarrollo de un sistema destinado a determinar las regiones de agarre de objetos cilíndricos, específicamente botellas plásticas, en un entorno semi-estructurado utilizando visión por computadora. A pesar de la diversidad de formas, tamaños y colores que presentan las botellas, se asume un tamaño promedio de 500 ml para la investigación. El proyecto tiene como objetivo abordar desafíos en la manipulación robótica y la automatización, especialmente en aplicaciones industriales. Se inicia con la creación de un banco de imágenes que sirve como base para un sistema de procesamiento de imágenes, el cual, junto con herramientas de inteligencia artificial, permite entrenar una red neuronal específica para la tarea de agarre. La presente investigación profundiza en los métodos y tecnologías utilizados en la planificación de agarre y la manipulación robótica, destacando el uso de técnicas de aprendizaje profundo. El documento se encuentra organizado en capítulos que abarcan el contexto de la investigación, la motivación, el trabajo relacionado, los objetivos específicos y el desarrollo del sistema para la generación automática de regiones de agarre basadas en visión por computadora y aprendizaje automático. En el marco del desarrollo de la presente investigación, se centró en el análisis físico de un número determinado de objetos dispuestos en escena y las características físicas y funcionales de un gripper de dos dedos empleado para ejecutar una tarea de agarre específica. A partir de un sistema de percepción visual bidimensional ajustado y la extracción de características geométricas de los objetos, se diseñó e implementó un sistema algorítmico capaz de establecer regiones de agarre a lo largo de los objetos empleados. Posteriormente, se estableció un número de parámetros de evaluación heurísticos con el objetivo de determinar la viabilidad de cada una de las regiones encontradas sobre cada objeto en relación con su espacio circundante. (Texto tomado de la fuente).This master's thesis focuses on the development of a system aimed at determining the grasping regions of cylindrical objects, specifically plastic bottles, in a semi-structured environment using computer vision. Despite the diversity of shapes, sizes, and colors that bottles present, an average size of 500 ml is assumed for the research. The project aims to address challenges in robotic manipulation and automation, especially in industrial applications. It begins with the creation of an image bank that serves as the basis for an image processing system, which, along with artificial intelligence tools, allows the training of a specific neural network for the grasping task. This research delves into the methods and technologies used in grasp planning and robotic manipulation, highlighting the use of deep learning techniques. The document is organized into chapters that cover the context of the research, the motivation, related work, specific objectives, and the development of the system for the automatic generation of grasping regions based on computer vision and machine learning. In the framework of the development of this research, the focus was on the physical analysis of a determined number of objects arranged in the scene and the physical and functional characteristics of a two-finger gripper used to perform a specific grasping task. Based on an adjusted two-dimensional visual perception system and the extraction of geometric characteristics of the objects, an algorithmic system was designed and implemented to establish grasping regions along the employed objects. Subsequently, a number of heuristic evaluation parameters were established to determine the feasibility of each of the regions found on each object in relation to its surrounding space.MaestríaMagíster en Ingeniería - Automatización IndustrialRobótica industrial y graspingviii, 89 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Automatización IndustrialFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaAgarreAprendizaje profundoManipuladorVisión de máquinaGraspingDeep learningManipulatorMachine visionInteligencia artificialPropiedad físicaArtificial intelligencePhysical propertiesvisión artificialcomputer visionDiseño de un sistema que determine regiones de agarre de objetos cilíndricos en un entorno semi-estructurado basado en visiónDesign of a system that determines grasping regions of cylindrical objects in a semi-structured vision-based environmentTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAdarsh, Pranav ; Rathi, Pratibha ; Kumar, Manoj: YOLO v3-Tiny: Object Detection and Recognition using one stage improved model. En: 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS) (2020), 3, p. 687–694. ISBN 9781728151977Amend, John R. ; Brown, Eric ; Rodenberg, Nicholas ; Jaeger, Heinrich M. ; Lipson, Hod: A positive pressure universal gripper based on the jamming of granular material. En: IEEE Transactions on Robotics 28 (2012), 4, Nr. 2, p. 341–350. – ISSN 15523098Barequet, Gill ; Wolfers, Barbara: Optimizing a strip separating two polygons. En: Graphical Models and Image Processing 60 (1998), Nr. 3, p. 214–221. – ISSN 10773169Bhattacharya, Binay ; Toussaint, Godfried: Computing shortest transversals. En: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 510 LNCS (1991), p. 649–660. – ISBN 9783540542339Bhattacharya, Binay K. ; Toussaint, Godfried T.: Efficient algorithms for computing the maximum distance between two finite planar sets. En: Journal of Algorithms 4 (1983), 6, Nr. 2, p. 121–136. – ISSN 0196–6774Bohg, J ; Morales, A ; Asfour, T: Data-driven grasp synthesis—a survey. En: IEEE Transactions on robotics 30 (2014), p. 289–309Cebollada, S ; Payá L ; Flores, M ; . . . , A Peidró Expert Systems w. ; 2021, undefined: A state-of-the-art review on mobile robotics tasks using artificial intelligence and visual data. En: Expert Systems with Applications 167Dai, Jifeng ; He, Kaiming ; Sun, Jian: Convolutional feature masking for joint object and stuff segmentation. En: Computer Vision and Pattern Recognition 07-12-June-2015 (2014), 10, p. 3992–4000. – ISBN 9781467369640Deimel, Raphael ; Brock, Oliver: A novel type of compliant and underactuated robotic hand for dexterous grasping. En: https://doi.org/10.1177/0278364915592961 35 (2015), 8, Nr. 1-3, p. 161–185. – ISSN 17413176Deng, Z ; Zheng, X ; Zhang, L ; Zhang, J: A learning framework for semantic reach-to-grasp tasks integrating machine learning and optimization. En: Robotics and Autonomous Systems 108 (2018), p. 140–152Detry, Renaud ; Ek, Carl H. ; Madry, Marianna ; Piater, Justus ; Kragic, Danica: Generalizing grasps across partly similar objects. En: 2012 IEEE International Conference on Robotics and Automation, IEEE, 5 2012. – ISBN 978–1–4673–1405–3, p. 3791–3797Domae, Yukiyasu ; Okuda, Haruhisa ; Taguchi, Yuichi ; Sumi, Kazuhiko ; Hirai, Takashi: Fast graspability evaluation on single depth maps for bin picking with general grippers. En: 2014 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 5 2014. – ISBN 978–1–4799–3685–4, p. 1997–2004Eberly, David: Minimum-Area Rectangle Containing a Set of Points. En: Geometric Tools, LLC (2015)Eizicovits, D ; Berman, S: Automatic graspability map generation based on shape-primitives for unknown and familiar objects. En: Robotics and Autonomous Systems (2018)Erhan, Dumitru ; Szegedy, Christian ; Toshev, Alexander ; Anguelov, Dragomir: Scalable Object Detection using Deep Neural Networks. En: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2013), 12, p. 2155–2162. – ISBN 9781479951178Farabet, Clement ; Couprie, Camille ; Najman, Laurent ; Lecun, Yann: Learning hierarchical features for scene labeling. En: IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (2013), Nr. 8, p. 1915–1929. – ISSN 01628828Fogel, I ; Sagi, D: Biological Cybernetics Gabor Filters as Texture Discriminator. En: Biol. Cybern 61 (1989), p. 113Girshick, Ross ; Donahue, Jeff ; Darrell, Trevor ; Malik, Jitendra: Region-based Convolutional Networks for Accurate Object Detection and Segmentation. En: IEEE transactions on pattern analysis and machine intelligence 38 (2015), p. 142–158Goldfeder, C ; Ciocarlie, M ; Dang, H ; Allen, PK: The columbia grasp database. En: IEEE international conference on robotics and automation (2009), p. 1710–1716Gouda, Anas ; Ghanem, Abraham ; Kaiser, Pascal ; Hompel, Michael T.: Object class-agnostic segmentation for practical CNN utilization in industry. En: 2021 6th International Conference on Mechanical Engineering and Robotics Research, ICMERR 2021 (2021), p. 97–105. ISBN 9781665406420Gouda, Anas ; Roidl, Moritz: DoUnseen: Tuning-Free Class-Adaptive Object Detection of Unseen Objects for Robotic Grasping. En: arXiv preprint arXiv:2404.06277 (2023)Houle, Michael E. ; Toussaint, Godfried T.: Computing the Width of a Set. En: IEEE Transactions on Pattern Analysis and Machine Intelligence 10 (1988), Nr. 5, p. 761–765. – ISSN 01628828Hu, Xiao d. ; Wang, Xin q. ; Meng, Fan j. ; Hua, Xia ; Yan, Yu j. ; Li, Yu y. ; Huang, Jing ; Jiang, Xun l.: Gabor-CNN for object detection based on small samples. En: Defence Technology 16 (2020), 12, Nr. 6, p. 1116–1129. – ISSN 22149147Hu, Zhongxu ; Tan, Runjia ; Zhou, Yanxin ; Woon, Junyang ; Lv, Chen: Template-Based Category-Agnostic Instance Detection for Robotic Manipulation. En: IEEE Robotics and Automation Letters 7 (2022), 10, Nr. 4, p. 12451–12458. – ISSN 23773766Hughes, Josie ; Culha, Utku ; Giardina, Fabio ; Guenther, Fabian ; Rosendo, Andre; Iida, Fumiya: Soft manipulators and grippers: A review. En: Frontiers Robotics AI 3 (2016), 11, Nr. NOV, p. 223168. – ISSN 22969144Ito, Seiji ; Yoshioka, Michifumi ; Omatu, Sigeru ; Kita, Kouji ; Kugo, Kouichi: An image segmentation method using histograms and the human characteristics of HSI color space for a scene image. En: Artificial Life and Robotics 2006 10:1 10 (2006), 7, Nr. 1, p. 6–10. – ISSN 1614–7456Kasper, Alexander ; Xue, Zhixing ; Dillmann, R ̈udiger: The KIT object models database: An object model database for object recognition, localization and manipulation in service robotics. En: The International Journal of Robotics Research 31 (2012), 7, Nr. 8, p. 927–934. – ISSN 0278–3649Kumra, Sulabh ; Kanan, Christopher: Robotic grasp detection using deep convolutional neural networks. En: IEEE International Conference on Intelligent Robots and Systems 2017 (2017), 12, p. 769 – 776. – ISBN 9781538626825Lenz, Ian ; Lee, Honglak ; Saxena, Ashutosh: Deep learning for detecting robotic grasps. En: The International Journal of Robotics Research 34 (2015), 4, Nr. 4-5, p. 705–724. – ISSN 0278–3649Li, Miao ; Hang, Kaiyu ; Kragic, Danica ; Billard, Aude: Dexterous grasping under shape uncertainty. En: Robotics and Autonomous Systems 75 (2016), 1, p. 352–364. – ISSN 0921–8890Liao, Ping-Sung ; Chen, Tse-Sheng ; Chung, Pau-Choo: A Fast Algorithm for Multilevel Thresholding. En: JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 17 (2001), p. 713–727Liu, Chunfang ; Sun, Fuchun ; Ban, Xiaojuan: An effective method for grasp planning on objects with complex geometry combining human experience and analytical approach. En: Science China Information Sciences 59 (2016), 11, Nr. 11, p. 112212. – ISSN 1674–733XLozano-P ́erez, Tom ́as: Spatial Planning: A Configuration Space Approach. En: IEEE Transactions on Computers C-32 (1983), Nr. 2, p. 108–120. – ISSN 00189340McGlone, J. C. ; Mikhail, Edward M. ; Bethel, James S. Manual of Photogrammetry, 5th Edition - Dialnet. 1 2004Mebatsion, H.K. ; Paliwal, J.: Machine vision based automatic separation of touching convex shaped objects. En: Computers in Industry 63 (2012), 9, Nr. 7, p. 723–730. – ISSN 0166–3615Mercier, Jean P. ; Garon, Mathieu ; Giguere, Philippe ; Lalonde, Jean F.: Deep Template-based Object Instance Detection. En: Proceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021 (2019), 11, p. 1506–1515. ISBN 9780738142661Mizushima, Kaori ; Oku, Takumi ; Suzuki, Yosuke ; Tsuji, Tokuo ; Watanabe, Tetsuyou: Multi-fingered robotic hand based on hybrid mechanism of tendon-driven and jamming transition. En: 2018 IEEE International Conference on Soft Robotics, RoboSoft 2018 (2018), 7, p. 376–381. ISBN 9781538645161Otsu, Nobuyuki: THRESHOLD SELECTION METHOD FROM GRAY-LEVEL HISTOGRAMS. En: IEEE Trans Syst Man Cybern SMC-9 (1979), Nr. 1, p. 62–66. – ISSN 00189472Park, Kiru ; Patten, Timothy ; Prankl, Johann ; Vincze, Markus: Multi-task template matching for object detection, segmentation and pose estimation using depth images. En: Proceedings - IEEE International Conference on Robotics and Automation 2019-May (2019), 5, p. 7207–7213. – ISBN 9781538660263Patil, Rajesh V. ; Reddy, Y. P.: An Autonomous Technique for Multi Class Weld Imperfections Detection and Classification by Support Vector Machine. En: Journal of Nondestructive Evaluation 40 (2021), 9, Nr. 3. – ISSN 15734862Pfeifer, Rolf ; Lungarella, Max ; Iida, Fumiya: The challenges ahead for bio-inspired ’soft’ robotics. En: Communications of the ACM 55 (2012), 11, Nr. 11, p. 76–87. – ISSN 00010782Pirzadeh, Hormoz. ; Toussaint, Godfried T.: Computational geometry with the rotating calipers, McGill University, Tesis de Grado, 1999Prattichizzo, Domenico ; Malvezzi, Monica ; Gabiccini, Marco ; Bicchi, Antonio: On the manipulability ellipsoids of underactuated robotic hands with compliance. En: Robotics and Autonomous Systems 60 (2012), 3, Nr. 3, p. 337–346. – ISSN 0921–8890Ramli, S ; Mustafa, M M. ; Wahab, D A. ; Hussain, A: Plastic bottle shape classification using partial erosion-based approach. En: 2010 6th International Colloquium on Signal Processing & its Applications, IEEE, 5 2010. – ISBN 978–1–4244–7121–8, p. 1–4Redmon, Joseph ; Angelova, Anelia: Real-time grasp detection using convolutional neural networks. En: 2015 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 5 2015. – ISBN 978–1–4799–6923–4, p. 1316–1322Redmon, Joseph ; Farhadi, Ali: YOLO9000: Better, Faster, Stronger. En: Computer Vision and Pattern Recognition 2017-January (2016), 11, p. 6517–6525. ISBN 9781538604571Rich, Steven I. ; Wood, Robert J. ; Majidi, Carmel: Untethered soft robotics. En: Nature Electronics 2018 1:2 1 (2018), 2, Nr. 2, p. 102–112. – ISSN 2520–1131Roa, Máximo A. ; Suárez, Raúl: Grasp quality measures: review and performance. En: Autonomous Robots 38 (2015), 1, Nr. 1, p. 65–88. – ISSN 0929–5593Robert, Jean M. ; Toussaint, Godfried T.: Linear approximation of simple objects. En: Computational Geometry: Theory and Applications 4 (1994), Nr. 1, p. 27–52. – ISSN 0925–7721Sahbani, A ; El-Khoury, S ; Bidaud, P: An overview of 3D object grasp synthesis algorithms. En: Robotics and Autonomous Systems 60 (2012)Sankur, Bulent: Survey over image thresholding techniques and quantitative performance evaluation. En: Journal of Electronic Imaging 13 (2004), 1, Nr. 1, p. 146. – ISSN 1017–9909Saxena, A ; Driemeyer, J ; Ng, AY: Robotic grasping of novel objects using vision. En: The International Journal of Robotics Research 2 (2008), p. 157–163Scavino, Edgar ; Wahab, Dzuraidah A. ; Hussain, Aini ; Basri, Hassan ; Mustafa, Mohd M.: Application of automated image analysis to the identification and extraction of recyclable plastic bottles. En: Journal of Zhejiang University-SCIENCE A 10 (2009), 6, Nr. 6, p. 794–799. – ISSN 1673–565XSui, Dongbao ; Zhu, Yanhe ; Zhao, Sikai ; Wang, Tianshuo ; Agrawal, Sunil K. ; Zhang, He ; Zhao, Jie: A Bioinspired Soft Swallowing Gripper for Universal Adaptable Grasping. En: Soft Robotics 9 (2022), 2, Nr. 1, p. 36–56. – ISSN 21695180Tochon, G: Compression of color images.Toussaint, Godfried T. ; Toussaint, Godfried T.: A Simple Linear Algorithm for Intersecting Convex Polygons. En: THE VISUAL COMPUTER 1 (1985), p. 118–123Vahrenkamp, N ; Westkamp, L: Part-based grasp planning for familiar objects. En: 2016 IEEE-RAS 16th international conference on humanoid robots (humanoids) (2016), p. 919–925Wang, Yong ; Liu, Zhi ; Zhang, Yun: Image based adaptive coordinated control for cooperative manipulators. En: 2016 Chinese Control and Decision Conference (CCDC), IEEE, 5 2016. – ISBN 978–1–4673–9714–8, p. 5383–5388Wang, Zhaokun ; Peng, Binbin ; Huang, Yanjun ; Sun, Guanqun: Classification for plastic bottles recycling based on image recognition. En: Waste Management 88 (2019), 4, p. 170–181. – ISSN 0956–053XZhao, Gang ; Zou, Shuilong ; Wu, Huijie: Improved Algorithm for Face Mask Detection Based on YOLO-v4. En: International Journal of Computational Intelligence Systems 16 (2023), Nr. 3, p. 104. ISBN 0123456789Zhu, Jie ; Yu, Jian ; Wang, Chaomurilige ; Li, Fan-Zhang: Object recognition via contextual color attention. En: Journal of Visual Communication and Image Representation 27 (2015), 2, p. 44–56. – ISSN 10473203EstudiantesInvestigadoresLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86555/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1065604452.2024.pdf1065604452.2024.pdfTesis de Maestría en Ingeniería - Automatización Industrialapplication/pdf50429484https://repositorio.unal.edu.co/bitstream/unal/86555/2/1065604452.2024.pdf09953bd037c9e06eff09289b75d93108MD52THUMBNAIL1065604452.2024.pdf.jpg1065604452.2024.pdf.jpgGenerated Thumbnailimage/jpeg4848https://repositorio.unal.edu.co/bitstream/unal/86555/3/1065604452.2024.pdf.jpge05099dc238959a6ac8a6e52fa8a1c19MD53unal/86555oai:repositorio.unal.edu.co:unal/865552024-07-18 23:04:28.143Repositorio Institucional Universidad Nacional de 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