Manipulación visual-táctil para la recogida de residuos domésticos en exteriores

Vista sencilla de ítem
dc.contributor.authorCastaño-Amorós, Julioes_ES
dc.contributor.authorPáez-Ubieta, Ignacio de Loyolaes_ES
dc.contributor.authorGil, Pabloes_ES
dc.contributor.authorPuente, Santiago Timoteoes_ES
dc.contributor.funderGeneralitat Valencianaes_ES
dc.contributor.funderEuropean Regional Development Fundes_ES
dc.date.accessioned2023-04-18T12:17:30Z
dc.date.available2023-04-18T12:17:30Z
dc.date.issued2023-03-31
dc.description.abstract[EN] This work presents a perception system applied to robotic manipulation, that is able to assist in navegation, household waste classification and collection in outdoor environments. This system is made up of optical tactile sensors, RGBD cameras and a LiDAR. These sensors are integrated on a mobile platform with a robot manipulator and a robotic gripper. Our system is divided in three software modules, two of them are vision-based and the last one is tactile-based. The vision-based modules use CNNs to localize and recognize solid household waste, together with the grasping points estimation. The tactile-based module, which also uses CNNs and image processing, adjusts the gripper opening to control the grasping from touch data. Our proposal achieves localization errors around 6 %, a recognition accuracy of 98% and ensures the grasping stability the 91% of the attempts. The sum of runtimes of the three modules is less than 750 ms.en_EN
dc.description.abstract[ES] Este artículo presenta un sistema de percepcion orientado a la manipulación robótica, capaz de asistir en tareas de navegación, clasificacion y recogida de residuos domésticos en exterior. El sistema está compuesto de sensores táctiles ópticos, cámaras RGBD y un LiDAR. Estos se integran en una plataforma móvil que transporta un robot manipulador con pinza. El sistema consta de tres modulos software, dos visuales y uno táctil. Los módulos visuales implementan arquitecturas CNNs para la localización y reconocimiento de residuos sólidos, además de estimar puntos de agarre. El módulo táctil, también basado en CNNs y procesamiento de imagen, regula la apertura de la pinza para controlar el agarre a partir de informacion de contacto. Nuestra propuesta tiene errores de localizacion entorno al 6 %, una precisión de reconocimiento del 98 %, y garantiza estabilidad de agarre el 91 % de las veces. Los tres modulos trabajan en tiempos inferiores a los 750 ms.es_ES
dc.description.accrualMethodOJSes_ES
dc.description.bibliographicCitationCastaño-Amorós, J.; Páez-Ubieta, IDL.; Gil, P.; Puente, ST. (2023). Manipulación visual-táctil para la recogida de residuos domésticos en exteriores. Revista Iberoamericana de Automática e Informática industrial. 20(2):163-174. https://doi.org/10.4995/riai.2022.18534es_ES
dc.description.issue2es_ES
dc.description.referencesAltikat, A., Gulbe, A., Altikat, S., 2022. Intelligent solid waste classification using deep convolutional neural networks. Int. J. Environmental Science and Technology 19, 1285-1292. https://doi.org/10.1007/s13762-021-03179-4es_ES
dc.description.referencesBircanoglu, C., Atay, M.and Beser, F., Genc¸, , Kızrak, M. A., 2018. Recyclenet: Intelligent waste sorting using deep neural networks. In: Innovations in intelligent systems and applications. pp. 1-7. https://doi.org/10.1109/INISTA.2018.8466276es_ES
dc.description.referencesBohg, J., Morales, A., Asfour, T., Kragic, D., 2013. Data-driven grasp synthesis- a survey. IEEE Transactions on robotics 30 (2), 289-309. https://doi.org/10.1109/TRO.2013.2289018es_ES
dc.description.referencesBolya, D., Zhou, C., Xiao, F., Lee, Y., 2019. Yolact: Real-time instance segmentation. In: IEEE/CVF Int. Conf. on Computer Vision. pp. 9157-9166. https://doi.org/10.1109/ICCV.2019.00925es_ES
dc.description.referencesCastaño-Amoros, J., Gil, P., Puente, S., 2021. Touch detection with low-cost visual-based sensor. In: 2nd Int. Conf. on Robotics, Computer Vision and Intelligent Systems. pp. 136-142. https://doi.org/10.5220/0010699800003061es_ES
dc.description.referencesDe Gea, V., Puente, S., Gil, P., 2021. Domestic waste detection and grasping points for robotic picking up. 10.48550/arXiv.2105.06825, iEEE Int. Conf. on Robotics and Automation. Workshop: Emerging paradigms for robotic manipulation: from the lab to the productive world.es_ES
dc.description.referencesDel Pino, I., Muñoz-Bañon, M., Cova-Rocamora, S., Contreras, M., Candelas, F., Torres, F., 2020. Deeper in blue. Journal of Intelligent & Robotics Systems 98, 207-225. https://doi.org/10.1007/s10846-019-00983-6es_ES
dc.description.referencesDonlon, E., Dong, S., Liu, M., Li, J., Adelson, E., Rodriguez, A., 2018. Gelslim: A high-resolution, compact, robust, and calibrated tactile-sensing finger. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 1927-1934. https://doi.org/10.1109/IROS.2018.8593661es_ES
dc.description.referencesFeng, J., Tang, X., Jiang, X., Chen, Q., 2021. Garbage disposal of complex background based on deep learning with limited hardware resources. IEEE Sensors Journal 21(8), 21050-21058. https://doi.org/10.1109/JSEN.2021.3100636es_ES
dc.description.referencesFu, B., Li, S., Wei, J., Li, Q., Wang, Q., T. J., 2021. A novel intelligent garbage classification system based on deep learning and an embedded linux system. IEEE Access 9), 131134-131146. https://doi.org/10.1109/ACCESS.2021.3114496es_ES
dc.description.referencesGuo, N., Zhang, B., Zhou, J., Zhan, K., Lai, S., 2020. Pose estimation and adaptable grasp configuration with point cloud registration and geometry understanding for fruit grasp planning. Computers and Electronics in Agriculture 179, 105818. https://doi.org/10.1016/j.compag.2020.105818es_ES
dc.description.referencesHe, K., Zhang, X., Ren, S., Sun, J., 2021. Deep residual learning for image recognition. In: IEEE Conf. on Computer Vision And Pattern Recognition. https://doi.org/10.1109/CVPR.2016.90es_ES
dc.description.referencesJiang, D., Li, G., Sun, Y., Hu, J., Yun, J., Liu, Y., 2021. Manipulator grabbing position detection with information fusion of color image and depth image using deep learning. Journal of Ambient Intelligence and Humanized Computing 12 (12), 10809-10822. https://doi.org/10.1007/s12652-020-02843-wes_ES
dc.description.referencesKim, D., Li, A., Lee, J., 2021. Stable robotic grasping of multiple objects using deep neural networks. Robotica 39 (4), 735-748. https://doi.org/10.1017/S0263574720000703es_ES
dc.description.referencesKiyokawa, T., Katayama, H., Tatsuta, Y., Takamatsu, J., Ogasawara, T., 2021. Robotic waste sorter with agile manipulation and quickly trainable detector. IEEE Access 9), 124616-124631. https://doi.org/10.1109/ACCESS.2021.3110795es_ES
dc.description.referencesKolamuri, R., Si, Z., Zhang, Y., Agarwal, A., Yuan, W., 2021. Improving grasp stability with rotation measurement from tactile sensing. In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 6809-6816. https://doi.org/10.1109/IROS51168.2021.9636488es_ES
dc.description.referencesLambeta, Chou, P.-W., Tian, S., Yang, B., Maloon, B., Most, V., Stroud, D., Santos, R., B.-A., Kammerer, G., Jayaraman, D., Calandra, R., 2020. Digit: A novel design for a low-cost compact high-resolution tactile sensor with application to in-hand manipulation. IEEE Robotics and Automation Letters 5(3), 3838-38451. https://doi.org/10.1109/LRA.2020.2977257es_ES
dc.description.referencesLin, Y., Lloyd, J., Church, A., Lepora, N. F., 2022. Tactile gym 2.0: Sim-to-real deep reinforcement learning for comparing low-cost high-resolution robot touch. IEEE Robotics and Automation Letters 7 (4), 10754-10761. https://doi.org/10.1109/LRA.2022.3195195es_ES
dc.description.referencesLiu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., Pietikainen, M., 2020. Deep learning for generic object detection: A survey. Int. J. of Computer Vision 128, 261--318. https://doi.org/10.1007/s11263-019-01247-4es_ES
dc.description.referencesLiu, Y., Jiang, D., Duan, H., Sun, Y., Li, G., Tao, B., Yun, J., Liu, Y., Chen, B., 2021. Dynamic gesture recognition algorithm based on 3d convolutional neural network. Computational Intelligence and Neuroscience 2021. https://doi.org/10.1155/2021/4828102es_ES
dc.description.referencesMinaee, S., Boykov, Y., Porikli, F., Plaza, A., Kehtarnavaz, N., Terzopoulos, D., 2020. Image segmentation using deep learning: A survey. IEEE Trans on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/TPAMI.2021.3059968es_ES
dc.description.referencesNewbury, R., Gu, M., Chumbley, L., Mousavian, A., Eppner, C., Leitner, J., Bohg, J., Morales, A., Asfour, T., Kragic, D., et al., 2022. Deep learning approaches to grasp synthesis: A review. arXiv preprint arXiv:2207.02556.es_ES
dc.description.referencesPatrizi, A., Gambosi, G., Zanzotto, F., 2021. Data augmentation using background replacement for automated sorting of littered waste. J. of Imaging 7(8), 144. https://doi.org/10.3390/jimaging7080144es_ES
dc.description.referencesRedmon, J., 2014. Darknet: Open source neural networks in c. http://pjreddie.com/darknet/.es_ES
dc.description.referencesSahbani, A., El-Khoury, S., Bidaud, P., 2012. An overview of 3d object grasp synthesis algorithms. Robotics and Autonomous Systems 60 (3), 326-336. https://doi.org/10.1016/j.robot.2011.07.016es_ES
dc.description.referencesSandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C., 2018. Mobilenetv2: Inverted residuals and linear bottlenecks. In: IEEE Conf. on Computer Vision and Pattern Recognition. pp. 4510-4520. https://doi.org/10.1109/CVPR.2018.00474es_ES
dc.description.referencesSandykbayeva, D., Kappassov, Z., Orazbayev, B., 2022. Vibrotouch: Active tactile sensor for contact detection and force sensing via vibrations. Sensors 22 (17). https://doi.org/10.3390/s22176456es_ES
dc.description.referencesShaw-Cortez, W., Oetomo, D., Manzie, C., Choong, P., 2018. Tactile-based blind grasping: A discrete-time object manipulation controller for robotic hands. IEEE Robotics and Automation Letters 3 (2), 1064-1071. https://doi.org/10.1109/LRA.2018.2794612es_ES
dc.description.referencesSimonyan, K., Zisserman, A., 2015. Very deep convolutional networks for large-scale image recognition. In: 3rd Int. Conf. on Learning Representations. DOI: https://doi.org/10.48550/arXiv.1409.1556es_ES
dc.description.referencesSuárez, R., Palomo-Avellaneda, L., Martínez, J., Clos, D., García, N., 2020. Manipulador móvil, bibrazo y diestro con nuevas ruedas omnidireccionales. Revista Iberoamericana de Automática e Informática industrial 17 (1), 10-21. https://doi.org/10.4995/riai.2019.11422es_ES
dc.description.referencesSzegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z., 2016. Rethinking the inception architecture for computer vision. In: IEEE Conf. on Computer Vision and Pattern Recognition. pp. 2818-2826. https://doi.org/10.1109/CVPR.2016.308es_ES
dc.description.referencesVelasco, E., Zapata-Impata, B. S., Gil, P., Torres, F., 2020. Clasificación de objetos usando percepción bimodal de palpación única en acciones de agarre robótico. Revista Iberoamericana de Automática e Informática Industrial 17 (1) , 44-55. https://doi.org/10.4995/riai.2019.10923es_ES
dc.description.referencesVo, A. H., Son, L., Vo, M., Le, T., 2019. A novel framework for trash classification using deep transfer learning. IEEE Access 7, 178631-178639. https://doi.org/10.1109/ACCESS.2019.2959033es_ES
dc.description.referencesWard-Cherrier, B., Pestell, N., Cramphorn, L., Winstone, B., Giannaccini, M. E., Rossiter, J., Lepora, N. F., 2018. The tactip family: Soft optical tactile sensors with 3d-printed biomimetic morphologies. Soft robotics 5 (2), 216-227. https://doi.org/10.1089/soro.2017.0052es_ES
dc.description.referencesYao, T., Guo, X., Li, C., Qi, H., Lin, H., Liu, L., Dai, Y., Qu, L., Huang, Z., Liu, P., et al., 2020. Highly sensitive capacitive flexible 3d-force tactile sensors for robotic grasping and manipulation. Journal of Physics D: Applied Physics 53 (44), 445109. https://doi.org/10.1088/1361-6463/aba5c0es_ES
dc.description.referencesYuan, W., Dong, S., Adelson, E. H., 2017. Gelsight: High-resolution robot tactile sensors for estimating geometry and force. Sensors 17 (12), 2762. https://doi.org/10.3390/s17122762es_ES
dc.description.referencesZapata-Impata, B., Gil, P., Pomares, J., Torres, F., 2019a. Fast geometry-based computation of grasping points on three-dimensional point clouds. Int. J. of Advanced Robotic Systems, 1-18. https://doi.org/10.1177/1729881419831846es_ES
dc.description.referencesZapata-Impata, B. S., Gil, P., Torres, F., 2019b. Learning spatio temporal tactile features with a convlstm for the direction of slip detection. Sensors 19 (3), 523. https://doi.org/10.3390/s19030523es_ES
dc.description.sponsorshipEste trabajo ha sido financiado con Fondos Europeos de Desarrollo Regional (FEDER), el gobierno de la Generalitat Valenciana a través del proyecto PROMETEO/2021/075, y los recursos computaciones fueron financiados a traves de la ayuda IDIFEDER/2020/003.es_ES
dc.description.upvformatpfin174es_ES
dc.description.upvformatpinicio163es_ES
dc.description.volume20es_ES
dc.identifier.doi10.4995/riai.2022.18534
dc.identifier.eissn1697-7920
dc.identifier.issn1697-7912
dc.identifier.urihttps://riunet.upv.es/handle/10251/192800
dc.languageEspañoles_ES
dc.publisherUniversitat Politècnica de Valènciaes_ES
dc.relation.ispartofRevista Iberoamericana de Automática e Informática industriales_ES
dc.relation.pasarelaOJS\18534es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/GV//PROMETEO%2F2021%2F075es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/GV//IDIFEDER%2F2020%2F003es_ES
dc.relation.publisherversionhttps://doi.org/10.4995/riai.2022.18534es_ES
dc.relation.references10.1007/s13762-021-03179-4es_ES
dc.relation.references10.1109/INISTA.2018.8466276es_ES
dc.relation.references10.1109/TRO.2013.2289018es_ES
dc.relation.references10.1109/ICCV.2019.00925es_ES
dc.relation.references10.5220/0010699800003061es_ES
dc.relation.references10.1007/s10846-019-00983-6es_ES
dc.relation.references10.1109/IROS.2018.8593661es_ES
dc.relation.references10.1109/JSEN.2021.3100636es_ES
dc.relation.references10.1109/ACCESS.2021.3114496es_ES
dc.relation.references10.1016/j.compag.2020.105818es_ES
dc.relation.references10.1109/CVPR.2016.90es_ES
dc.relation.references10.1007/s12652-020-02843-wes_ES
dc.relation.references10.1017/S0263574720000703es_ES
dc.relation.references10.1109/ACCESS.2021.3110795es_ES
dc.relation.references10.1109/IROS51168.2021.9636488es_ES
dc.relation.references10.1109/LRA.2020.2977257es_ES
dc.relation.references10.1109/LRA.2022.3195195es_ES
dc.relation.references10.1007/s11263-019-01247-4es_ES
dc.relation.references10.1155/2021/4828102es_ES
dc.relation.references10.1109/TPAMI.2021.3059968es_ES
dc.relation.references10.1109/TRO.2023.3280597es_ES
dc.relation.references10.3390/jimaging7080144es_ES
dc.relation.references10.1016/j.robot.2011.07.016es_ES
dc.relation.references10.1109/CVPR.2018.00474es_ES
dc.relation.references10.3390/s22176456es_ES
dc.relation.references10.1109/LRA.2018.2794612es_ES
dc.relation.references10.4995/riai.2019.11422es_ES
dc.relation.references10.1109/CVPR.2016.308es_ES
dc.relation.references10.4995/riai.2019.10923es_ES
dc.relation.references10.1109/ACCESS.2019.2959033es_ES
dc.relation.references10.1089/soro.2017.0052es_ES
dc.relation.references10.1088/1361-6463/aba5c0es_ES
dc.relation.references10.3390/s17122762es_ES
dc.relation.references10.1177/1729881419831846es_ES
dc.relation.references10.3390/s19030523es_ES
dc.rightsReconocimiento - No comercial - Compartir igual (by-nc-sa)es_ES
dc.rights.accessRightsAbiertoes_ES
dc.subjectVisual detectiones_ES
dc.subjectObject recognitiones_ES
dc.subjectObject locationes_ES
dc.subjectTactile perceptiones_ES
dc.subjectRobotic manipulationes_ES
dc.subjectDetección visuales_ES
dc.subjectReconocimiento de objetoses_ES
dc.subjectLocalización de objetoses_ES
dc.subjectPercepción táctiles_ES
dc.subjectManipulación robóticaes_ES
dc.titleManipulación visual-táctil para la recogida de residuos domésticos en exterioreses_ES
dc.title.alternativeVisual-tactile manipulation to collect household waste in outdoores_ES
dc.typeArtículoes_ES
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_ES
dspace.entity.typePublication
upv.uuid72d663ea-d0f8-443c-a4eb-b0f54c01f031es_ES

Archivos

Bloque original

Mostrando 1 - 1 de 1
Miniatura
Nombre:
Castano-AmorosPaez-UbietaGil - Manipulacion visual-tactil para la recogida de residuos domesticos....pdf
Tamaño:
29.95 MB
Formato:
Adobe Portable Document Format
Descripción:
Versión editorial
[object Object] Descargar

Colecciones

Revista Iberoamericana de Automática e Informática Industrial - Vol 20, No 2 (2023)