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Vision for Robust Robot Manipulation

Advances in Robotics are leading to a new generation of assistant robots working in ordinary, domestic settings. This evolution raises new challenges in the tasks to be accomplished by the robots. This is the case for object manipulation where the detect-approach-grasp loop requires a robust recover...

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Detalles Bibliográficos
Autores principales: Martinez-Martin, Ester, del Pobil, Angel P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6480289/
https://www.ncbi.nlm.nih.gov/pubmed/30959920
http://dx.doi.org/10.3390/s19071648
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author Martinez-Martin, Ester
del Pobil, Angel P.
author_facet Martinez-Martin, Ester
del Pobil, Angel P.
author_sort Martinez-Martin, Ester
collection PubMed
description Advances in Robotics are leading to a new generation of assistant robots working in ordinary, domestic settings. This evolution raises new challenges in the tasks to be accomplished by the robots. This is the case for object manipulation where the detect-approach-grasp loop requires a robust recovery stage, especially when the held object slides. Several proprioceptive sensors have been developed in the last decades, such as tactile sensors or contact switches, that can be used for that purpose; nevertheless, their implementation may considerably restrict the gripper’s flexibility and functionality, increasing their cost and complexity. Alternatively, vision can be used since it is an undoubtedly rich source of information, and in particular, depth vision sensors. We present an approach based on depth cameras to robustly evaluate the manipulation success, continuously reporting about any object loss and, consequently, allowing it to robustly recover from this situation. For that, a Lab-colour segmentation allows the robot to identify potential robot manipulators in the image. Then, the depth information is used to detect any edge resulting from two-object contact. The combination of those techniques allows the robot to accurately detect the presence or absence of contact points between the robot manipulator and a held object. An experimental evaluation in realistic indoor environments supports our approach.
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spelling pubmed-64802892019-04-29 Vision for Robust Robot Manipulation Martinez-Martin, Ester del Pobil, Angel P. Sensors (Basel) Article Advances in Robotics are leading to a new generation of assistant robots working in ordinary, domestic settings. This evolution raises new challenges in the tasks to be accomplished by the robots. This is the case for object manipulation where the detect-approach-grasp loop requires a robust recovery stage, especially when the held object slides. Several proprioceptive sensors have been developed in the last decades, such as tactile sensors or contact switches, that can be used for that purpose; nevertheless, their implementation may considerably restrict the gripper’s flexibility and functionality, increasing their cost and complexity. Alternatively, vision can be used since it is an undoubtedly rich source of information, and in particular, depth vision sensors. We present an approach based on depth cameras to robustly evaluate the manipulation success, continuously reporting about any object loss and, consequently, allowing it to robustly recover from this situation. For that, a Lab-colour segmentation allows the robot to identify potential robot manipulators in the image. Then, the depth information is used to detect any edge resulting from two-object contact. The combination of those techniques allows the robot to accurately detect the presence or absence of contact points between the robot manipulator and a held object. An experimental evaluation in realistic indoor environments supports our approach. MDPI 2019-04-06 /pmc/articles/PMC6480289/ /pubmed/30959920 http://dx.doi.org/10.3390/s19071648 Text en © 2019 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Martinez-Martin, Ester
del Pobil, Angel P.
Vision for Robust Robot Manipulation
title Vision for Robust Robot Manipulation
title_full Vision for Robust Robot Manipulation
title_fullStr Vision for Robust Robot Manipulation
title_full_unstemmed Vision for Robust Robot Manipulation
title_short Vision for Robust Robot Manipulation
title_sort vision for robust robot manipulation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6480289/
https://www.ncbi.nlm.nih.gov/pubmed/30959920
http://dx.doi.org/10.3390/s19071648
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