Vision for Robust Robot Manipulation
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Title
Vision for Robust Robot ManipulationDate
2019Publisher
MDPIISSN
1424-8220; 1424-8220Bibliographic citation
Martinez-Martin, Ester; del Pobil, Angel P. "Vision for Robust Robot Manipulation." Sensors, 2019, vol. 19, núm. 7, p. 1648Type
info:eu-repo/semantics/articlePublisher version
https://www.mdpi.com/1424-8220/19/7/1648Version
info:eu-repo/semantics/publishedVersionSubject
Abstract
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 ... [+]
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. [-]
Is part of
Sensors, 2019, vol. 19, núm. 7, p. 1648Investigation project
This research was partially funded by Ministerio de Economía y Competitividad grant number DPI2015-69041-R. This paper describes research done at UJI Robotic Intelligence Laboratory. Support for this laboratory is provided in part by Ministerio de Economía y Competitividad and by Universitat Jaume I (UJI-B2018-74).Rights
info:eu-repo/semantics/openAccess
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Except where otherwise noted, this item's license is described as © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
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(CC BY) license (http://creativecommons.org/licenses/by/4.0/).