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(MARGOT) Monocular Camera-Based Robot Grasping Strategy for Metallic Objects
dc.contributor.author | Veiga Almagro, Carlos | |
dc.contributor.author | Munoz, Renato | |
dc.contributor.author | García González, Álvaro | |
dc.contributor.author | Matheson, Eloise | |
dc.contributor.author | Marin, Raul | |
dc.contributor.author | Di Castro, Mario | |
dc.contributor.author | FERRE PEREZ, MANUEL | |
dc.date.accessioned | 2023-07-24T07:36:26Z | |
dc.date.available | 2023-07-24T07:36:26Z | |
dc.date.issued | 2023-06-05 | |
dc.identifier.citation | Veiga Almagro, Carlos, Renato Andrés Muñoz Orrego, Álvaro García González, Eloise Matheson, Raúl Marín Prades, Mario Di Castro, and Manuel Ferre Pérez. 2023. "(MARGOT) Monocular Camera-Based Robot Grasping Strategy for Metallic Objects" Sensors 23, no. 11: 5344. https://doi.org/10.3390/s23115344 | ca_CA |
dc.identifier.issn | 1424-8220 | |
dc.identifier.uri | http://hdl.handle.net/10234/203522 | |
dc.description.abstract | 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. | ca_CA |
dc.format.extent | 27 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | MDPI | ca_CA |
dc.relation.isPartOf | Sensors 2023, 23, 5344. https://doi.org/10.3390/s23115344 | ca_CA |
dc.relation.uri | The data used in this study contain sensitive information regarding the processes at CERN, and as such, cannot be shared publicly due to privacy concerns. Nevertheless, researchers who seek to access this data may request access through the corresponding author. Requests will be evaluated on a case-by-case basis. While we recognize the importance of data sharing and transparency in research, protecting the privacy and confidentiality of our study participants remains our top priority. Therefore, we are committed to taking all necessary measures to ensure the ethical use of data and safeguarding the privacy of our participants. | |
dc.rights | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. | ca_CA |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | ca_CA |
dc.subject | computer vision | ca_CA |
dc.subject | telerobotics | ca_CA |
dc.subject | grasping determination | ca_CA |
dc.title | (MARGOT) Monocular Camera-Based Robot Grasping Strategy for Metallic Objects | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.3390/s23115344 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
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