(MARGOT) Monocular Camera-Based Robot Grasping Strategy for Metallic Objects
Ver/ Abrir
Impacto
Scholar |
Otros documentos de la autoría: Veiga Almagro, Carlos; Munoz, Renato; García González, Álvaro; Matheson, Eloise; Marin, Raul; Di Castro, Mario; FERRE PEREZ, MANUEL
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7036
comunitat-uji-handle3:10234/8620
comunitat-uji-handle4:
INVESTIGACIONMetadatos
Título
(MARGOT) Monocular Camera-Based Robot Grasping Strategy for Metallic ObjectsAutoría
Fecha de publicación
2023-06-05Editor
MDPIISSN
1424-8220Cita bibliográfica
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/s23115344Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
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 ... [+]
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. [-]
Publicado en
Sensors 2023, 23, 5344. https://doi.org/10.3390/s23115344Derechos de acceso
© 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
Aparece en las colecciones
- ICC_Articles [419]