Publication:
Correction of Visual Perception Based on Neuro-Fuzzy Learning for the Humanoid Robot TEO

Simple item page
dc.affiliation.dptoUC3M. Departamento de Ingeniería de Sistemas y Automáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Laboratorio de Robótica (Robotics Lab)es
dc.contributor.authorHernandez Vicen, Juan
dc.contributor.authorMartínez de la Casa Díaz, Santiago
dc.contributor.authorGarcía Haro, Juan Miguel
dc.contributor.authorBalaguer Bernaldo de Quirós, Carlos
dc.date.accessioned2019-02-15T10:12:52Z
dc.date.available2019-02-15T10:12:52Z
dc.date.issued2018-03-25
dc.description.abstractNew applications related to robotic manipulation or transportation tasks, with or without physical grasping, are continuously being developed. To perform these activities, the robot takes advantage of different kinds of perceptions. One of the key perceptions in robotics is vision. However, some problems related to image processing makes the application of visual information within robot control algorithms difficult. Camera-based systems have inherent errors that affect the quality and reliability of the information obtained. The need of correcting image distortion slows down image parameter computing, which decreases performance of control algorithms. In this paper, a new approach to correcting several sources of visual distortions on images in only one computing step is proposed. The goal of this system/algorithm is the computation of the tilt angle of an object transported by a robot, minimizing image inherent errors and increasing computing speed. After capturing the image, the computer system extracts the angle using a Fuzzy filter that corrects at the same time all possible distortions, obtaining the real angle in only one processing step. This filter has been developed by the means of Neuro-Fuzzy learning techniques, using datasets with information obtained from real experiments. In this way, the computing time has been decreased and the performance of the application has been improved. The resulting algorithm has been tried out experimentally in robot transportation tasks in the humanoid robot TEO (Task Environment Operator) from the University Carlos III of Madrid.en
dc.description.sponsorshipThe research leading to these results has received funding from the RoboCity2030-III-CM project (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. fase III; S2013/MIT-2748), funded by Programas de Actividades I + D en la Comunidad de Madrid and cofunded by Structural Funds of the EU.en
dc.format.extent23
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationHernandez-Vicen, J., Martinez, S., Garcia-Haro, J.M., Balaguer, C. (2018). Correction of Visual Perception Based on Neuro-Fuzzy Learning for the Humanoid Robot TEO. Sensors, 18 (4), 972.en
dc.identifier.doihttps://www.doi.org/10.3390/s18040972
dc.identifier.issn1424-8220
dc.identifier.publicationissue4
dc.identifier.publicationtitleSensorsen
dc.identifier.publicationvolume18
dc.identifier.urihttps://hdl.handle.net/10016/28074
dc.identifier.uxxiAR/0000021682
dc.language.isoengen
dc.publisherMDPIen
dc.relation.projectIDComunidad de Madrid. S2013/MIT-2748/RoboCity2030-III-CMprojectes
dc.rights© 2018 by the authors. Licensee MDPI, Basel, Switzerland.en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaRobótica e Informática Industriales
dc.subject.otherHumanoid roboten
dc.subject.otherArtificial visionen
dc.subject.otherNon-grasping manipulationen
dc.subject.otherNeuro-Fuzzy filteren
dc.subject.otherDistortion correctionen
dc.titleCorrection of Visual Perception Based on Neuro-Fuzzy Learning for the Humanoid Robot TEOen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
correction_sensors_2018.pdf
Size:
4.24 MB
Format:
Adobe Portable Document Format
Download
Collections
DISA - LR - Artículos de Revistas