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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...
Autores principales: | , , , , , , |
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Lenguaje: | eng |
Publicado: |
2023
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.3390/s23115344 http://cds.cern.ch/record/2866085 |
_version_ | 1780978078622679040 |
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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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