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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: Veiga Almagro, Carlos, Muñoz Orrego, Renato Andrés, García González, Álvaro, Matheson, Eloise, Marín Prades, Raúl, Di Castro, Mario, Ferre Pérez, Manuel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256039/
https://www.ncbi.nlm.nih.gov/pubmed/37300071
http://dx.doi.org/10.3390/s23115344
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author Veiga Almagro, Carlos
Muñoz Orrego, Renato Andrés
García González, Álvaro
Matheson, Eloise
Marín Prades, Raúl
Di Castro, Mario
Ferre Pérez, Manuel
author_facet Veiga Almagro, Carlos
Muñoz Orrego, Renato Andrés
García González, Álvaro
Matheson, Eloise
Marín Prades, Raúl
Di Castro, Mario
Ferre Pérez, Manuel
author_sort Veiga Almagro, Carlos
collection PubMed
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.
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spelling pubmed-102560392023-06-10 (MARGOT) Monocular Camera-Based Robot Grasping Strategy for Metallic Objects Veiga Almagro, Carlos Muñoz Orrego, Renato Andrés García González, Álvaro Matheson, Eloise Marín Prades, Raúl Di Castro, Mario Ferre Pérez, Manuel Sensors (Basel) Article 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. MDPI 2023-06-05 /pmc/articles/PMC10256039/ /pubmed/37300071 http://dx.doi.org/10.3390/s23115344 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Veiga Almagro, Carlos
Muñoz Orrego, Renato Andrés
García González, Álvaro
Matheson, Eloise
Marín Prades, Raúl
Di Castro, Mario
Ferre Pérez, Manuel
(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 Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256039/
https://www.ncbi.nlm.nih.gov/pubmed/37300071
http://dx.doi.org/10.3390/s23115344
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