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(MARGOT) Monocular Camera-Based Robot Grasping Strategy for Metallic Objects

Robotic handling of objects is not always a trivial assignment, even in teleoperation where, in most cases, this might lead to stressful labor for operators. To reduce the task difficulty, supervised motions could be performed in safe scenarios to reduce the workload in these non-critical steps by u...

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Autores principales: Almagro, Carlos Veiga, Orrego, Renato Andrés Muñoz, González, Álvaro García, Matheson, Eloise, Prades, Raúl Marín, Di Castro, Mario, Pérez, Manuel Ferre
Lenguaje:eng
Publicado: 2023
Materias:
Acceso en línea:https://dx.doi.org/10.3390/s23115344
http://cds.cern.ch/record/2866085
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author Almagro, Carlos Veiga
Orrego, Renato Andrés Muñoz
González, Álvaro García
Matheson, Eloise
Prades, Raúl Marín
Di Castro, Mario
Pérez, Manuel Ferre
author_facet Almagro, Carlos Veiga
Orrego, Renato Andrés Muñoz
González, Álvaro García
Matheson, Eloise
Prades, Raúl Marín
Di Castro, Mario
Pérez, Manuel Ferre
author_sort Almagro, Carlos Veiga
collection CERN
description Robotic handling of objects is not always a trivial assignment, even in teleoperation where, in most cases, this might lead to stressful labor for operators. To reduce the task difficulty, supervised motions could be performed in safe scenarios to reduce the workload in these non-critical steps by using machine learning and computer vision techniques. This paper describes a novel grasping strategy based on a groundbreaking geometrical analysis which extracts diametrically opposite points taking into account surface smoothing (even those target objects that might conform highly complex shapes) to guarantee the uniformity of the grasping. It uses a monocular camera, as we are often facing space restrictions that generate the need to use laparoscopic cameras integrated in the tools, to recognize and isolate targets from the background, estimating their spatial coordinates and providing the best possible stable grasping points for both feature and featureless objects. It copes with reflections and shadows produced by light sources (which require extra effort to extract their geometrical properties) in unstructured facilities such as nuclear power plants or particle accelerators on scientific equipment. Based on the experimental results, utilizing a specialized dataset improved the detection of metallic objects in low-contrast environments, resulting in the successful application of the algorithm with error rates in the scale of millimeters in the majority of repeatability and accuracy tests.
id cern-2866085
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2023
record_format invenio
spelling cern-28660852023-07-27T20:10:35Zdoi:10.3390/s23115344http://cds.cern.ch/record/2866085engAlmagro, Carlos VeigaOrrego, Renato Andrés MuñozGonzález, Álvaro GarcíaMatheson, EloisePrades, Raúl MarínDi Castro, MarioPérez, Manuel Ferre(MARGOT) Monocular Camera-Based Robot Grasping Strategy for Metallic ObjectsDetectors and Experimental TechniquesRobotic handling of objects is not always a trivial assignment, even in teleoperation where, in most cases, this might lead to stressful labor for operators. To reduce the task difficulty, supervised motions could be performed in safe scenarios to reduce the workload in these non-critical steps by using machine learning and computer vision techniques. This paper describes a novel grasping strategy based on a groundbreaking geometrical analysis which extracts diametrically opposite points taking into account surface smoothing (even those target objects that might conform highly complex shapes) to guarantee the uniformity of the grasping. It uses a monocular camera, as we are often facing space restrictions that generate the need to use laparoscopic cameras integrated in the tools, to recognize and isolate targets from the background, estimating their spatial coordinates and providing the best possible stable grasping points for both feature and featureless objects. It copes with reflections and shadows produced by light sources (which require extra effort to extract their geometrical properties) in unstructured facilities such as nuclear power plants or particle accelerators on scientific equipment. Based on the experimental results, utilizing a specialized dataset improved the detection of metallic objects in low-contrast environments, resulting in the successful application of the algorithm with error rates in the scale of millimeters in the majority of repeatability and accuracy tests.oai:cds.cern.ch:28660852023
spellingShingle Detectors and Experimental Techniques
Almagro, Carlos Veiga
Orrego, Renato Andrés Muñoz
González, Álvaro García
Matheson, Eloise
Prades, Raúl Marín
Di Castro, Mario
Pérez, Manuel Ferre
(MARGOT) Monocular Camera-Based Robot Grasping Strategy for Metallic Objects
title (MARGOT) Monocular Camera-Based Robot Grasping Strategy for Metallic Objects
title_full (MARGOT) Monocular Camera-Based Robot Grasping Strategy for Metallic Objects
title_fullStr (MARGOT) Monocular Camera-Based Robot Grasping Strategy for Metallic Objects
title_full_unstemmed (MARGOT) Monocular Camera-Based Robot Grasping Strategy for Metallic Objects
title_short (MARGOT) Monocular Camera-Based Robot Grasping Strategy for Metallic Objects
title_sort (margot) monocular camera-based robot grasping strategy for metallic objects
topic Detectors and Experimental Techniques
url https://dx.doi.org/10.3390/s23115344
http://cds.cern.ch/record/2866085
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