Listar por autor "caed28184827c7bfcd3727737b5ba7c2"
Mostrando ítems 1-19 de 19
-
Application of Computer Vision and Deep Learning in the railway domain for autonomous train stop operation
Arana-Arexolaleiba, Nestor (IEEE, 2020)The purpose of this paper is to present the results of the analysis of the application of Deep Learning in the railway domain with a particular focus on a train stop operation. The paper proposes an approach consisting of ... -
Deflectometric data segmentation for surface inspection: a fully convolutional neural network approach
Maestro-Watson, Daniel; Balzategui, Julen; Eciolaza, Luka; Arana-Arexolaleiba, Nestor (SPIE, 2020)The purpose of this paper is to explore the use of fully convolutional neural networks (FCN) to perform a semantic segmentation of deflectometric recordings for quality control of reflective surfaces. The proposed method ... -
Evaluación de la experiencia de uso de un entorno robótico industrial en realidad virtual
Apraiz Iriarte, Ainhoa; Lasa, Ganix; Arana-Arexolaleiba, Nestor; Serrano Muñoz, Antonio; Elguea, Íñigo (AEIPRO, 2022)Industry 4.0 is leading to a whole new level of process automation, thus redefining the role of humans, and altering existing jobs in yet unknown ways. Although the number of robots in the manufacturing industry has been ... -
Fear Field: Adaptive constraints for safe environment transitions in Shielded Reinforcement Learning
Odriozola Olalde, Haritz; Arana-Arexolaleiba, Nestor (CEUR-WS.org, 2023)Shielding methods for Reinforcement Learning agents show potential for safety-critical industrial applications. However, they still lack robustness on nominal safety, a key property for safety control systems. In the case ... -
FlexRQC: Model for a Flexible Robot-Driven Quality Control Station
González Tomé, Ander; Irigoyen Ceberio, Ibai; Ayala, Unai; Agirre, Joseba Andoni; Arana-Arexolaleiba, Nestor (Elsevier Ltd., 2020)As the investment on a dedicated quality control stations is not desirable for limited production batches. In general, those systems result in very optimised systems and the lack of flexibility since they are designed for ... -
Goal-Conditioned Reinforcement Learning within a Human-Robot Disassembly Environment
Arana-Arexolaleiba, Nestor (MDPI, 2022)The introduction of collaborative robots in industrial environments reinforces the need to provide these robots with better cognition to accomplish their tasks while fostering worker safety without entering into safety ... -
Image Enhancement using GANs for Monocular Visual Odometry
Zubieta Ansorregi, Jon; Etxeberria Garcia, Mikel; Zamalloa, Maider; Arana-Arexolaleiba, Nestor (IEEE, 2021)Drones, mobile robots, and autonomous vehicles use Visual Odometry (VO) to move around complex environments. ORB-SLAM or deep learning-based approaches like DF-VO are two of the state-of-the-art technics for monocular VO. ... -
Implementation analysis for a hybrid particle filter on an FPGA based smart camera
Zuriarrain Arcarazo, Iker; Arana-Arexolaleiba, Nestor (Scitepress, 2010)Design and development of embedded devices which perform computer vision related task presents many challenges, many of which stem from attempting to fit the complexity of many higher level vision algorithms into the ... -
Labor Induction failure prediction based on B-Mode Ultrasound Image Processing using Multiscale Local Binary Patterns
Arana-Arexolaleiba, Nestor; Izaguirre Altuna, Alberto (IEEE, 2016)Labor induction is defined as the artificial onset of labor for the purpose of vaginal birth. Cesarean section is one of the potential risks of labor induction as it occurs in about 20% of the inductions. A ripe cervix ... -
Labor Induction failure prediction using Gabor filterbanks and Center Symmetric Local Binary Patterns
Arana-Arexolaleiba, Nestor; Izaguirre Altuna, Alberto (IEEE, 2018)Labor induction is defined as the artificial stimulation of uterine contractions aimed to induce vaginal birth. Occurring in about 20% of pregnancies labor induction has become one the most commonly practiced procedures ... -
Learning and generalising object extraction skill for contact-rich disassembly tasks: an introductory study
Arana-Arexolaleiba, Nestor (Springer, 2021)Remanufacturing automation must be designed to be flexible and robust enough to overcome the uncertainties, conditions of the products, and complexities in the planning and operation of the processes. Machine learning ... -
Monocular visual odometry for underground railway scenarios
Etxeberria Garcia, Mikel; Labayen, Mikel; Eizaguirre, Fernando; Zamalloa, Maider; Arana-Arexolaleiba, Nestor (SPIE, 2021)In this paper, the application of monocular Visual Odometry (VO) solutions for underground train stopping operation are explored. In order to analyze if the application of monocular VO solutions in challenging environments as ... -
Novel automated interactive reinforcement learning framework with a constraint-based supervisor for procedural tasks
Elguea Aguinaco, Iñigo; Aguirre, Aitor; Izagirre, Unai; Inziarte Hidalgo, Ibai; Bogh, Simon; Arana-Arexolaleiba, Nestor (Elsevier, 2025)Learning to perform procedural motion or manipulation tasks in unstructured or uncertain environments poses significant challenges for intelligent agents. Although reinforcement learning algorithms have demonstrated positive ... -
Reinforcement learning for collaborative robotic contact-rich disassembly tasks
Serrano Muñoz, Antonio (Mondragon Unibertsitatea. Goi Eskola Politeknikoa, 2023)With the exponential growth of the world’s population and the resulting increase in consumption rates, the efficient treatment of end-of-life (EOL) products has become critical to mitigating environmental impacts. ... -
A review on reinforcement learning for contact-rich robotic manipulation tasks
Elguea, Íñigo; Arana-Arexolaleiba, Nestor; Serrano Muñoz, Antonio (Elsevier, 2023)Research and application of reinforcement learning in robotics for contact-rich manipulation tasks have exploded in recent years. Its ability to cope with unstructured environments and accomplish hard-to-engineer behaviors ... -
A Scalable and Unified Multi-Control Framework for KUKA LBR iiwa Collaborative Robots
Serrano Muñoz, Antonio; Elguea, Íñigo; Arana-Arexolaleiba, Nestor (IEEE, 2023)The trend towards industrialization and digitalization has led more and more companies to deploy robots in their manufacturing facilities. In the field of collaborative robotics, the KUKA LBR iiwa is one of the benchmark ... -
Shielded Reinforcement Learning: A review of reactive methods for safe learning
Arana-Arexolaleiba, Nestor (IEEE, 2023)Reinforcement Learning (RL) algorithms are showing promising results in simulated environments, but their replication in real physical applications, even more so in safety-critical applications, is not yet guaranteed. ... -
skrl: Modular and Flexible Library for Reinforcement Learning
Arana-Arexolaleiba, Nestor (ArXiv, 2022)skrl is an open-source modular library for reinforcement learning written in Python and designed with a focus on readability, simplicity, and transparency of algorithm implementations. Apart from supporting environments ... -
Visual Odometry in Challenging Environments: An Urban Underground Railway Scenario Case
Arana-Arexolaleiba, Nestor (IEEE, 2022)Localization is one of the most critical tasks for an autonomous vehicle, as position information is required to understand its surroundings and move accordingly. Visual Odometry (VO) has shown promising results in the ...
